library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readxl)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(NbClust)
library(mice)
## 
## Attaching package: 'mice'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     cbind, rbind
library(viridis)
## Loading required package: viridisLite
library(stargazer)
## 
## Please cite as: 
## 
##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

Analysis Structure

This markdown document contains the clustering procedure to group Countries based on a set of characteristics. The clustering is meant to reduce the number of parameters in the Bayesian Hierarchical Model for H5N1 Infection Fatality Ratios, so that instead of country-year specific parameters we have cluster-year specific parameters at most.

The characteristics used (at least in principle) for clustering are indicators found on WorldBank:

Depending on the variables’ behaviour as a group (collinearity) we might have to remove some in the actual clustering step.

The analysis will be structured as follows:

Part A

# Data Some means it only contains some countries (i.e. the ones on the WHO tracker)

library(readxl)
library(openxlsx)

data_some <- read.csv("./WorldData.csv")

# Examine the data

str(data_some)
## 'data.frame':    217 obs. of  15 variables:
##  $ Country                                              : chr  "Afghanistan" "Albania" "Algeria" "American Samoa" ...
##  $ Population_Total                                     : num  31833879 2902012 38723552 53286 77161 ...
##  $ Current_Health_Expenditure_.GDP                      : chr  "11.2426364" "6.305844005" "5.268283782" ".." ...
##  $ GDP_per_Capita                                       : chr  "451.4874436" "4493.153886" "4545.498223" "11705.50962" ...
##  $ UNDP_Multidim_Poverty_Ratio_.Pop                     : chr  "55.9" "0.7" "1.4" ".." ...
##  $ Poverty_Ratio_SocialLine_.Pop                        : chr  ".." "19.14" "17.6" ".." ...
##  $ Population_Rural                                     : chr  "23970533.76" "1308266.095" "11725015.71" "6643.761905" ...
##  $ Prevalence.of.HIV.total....of.population.ages.15.49. : chr  "0.1" "0.1" "0.1" ".." ...
##  $ Incidence.of.tuberculosis..per.100.000.people..      : chr  "187.8571429" "17.19047619" "69.47619048" "6.771428571" ...
##  $ Diabetes.prevalence....of.population.ages.20.to.79.  : chr  "9.25" "6.5" "7.05" "20.3" ...
##  $ Hospital_beds_1000ppl                                : chr  "0.417894737" "2.953888889" "1.699230769" ".." ...
##  $ Physicians_1000ppl                                   : chr  "0.245933333" "1.387615385" "1.4515" ".." ...
##  $ Death.rate..crude..per.1.000.people...SP.DYN.CDRT.IN.: num  8.13 7.82 4.73 5.34 3.62 ...
##  $ Rural.land.area..sq..km...AG.LND.TOTL.RU.K2.         : chr  "636173.2292" "27331.60161" "2305478.23" "198.2477988" ...
##  $ Surface.area..sq..km...AG.SRF.TOTL.K2.               : chr  "652860" "28750" "2381740.4" "200" ...
summary(data_some)
##    Country          Population_Total    Current_Health_Expenditure_.GDP
##  Length:217         Min.   :1.044e+04   Length:217                     
##  Class :character   1st Qu.:7.213e+05   Class :character               
##  Mode  :character   Median :6.015e+06   Mode  :character               
##                     Mean   :3.334e+07                                  
##                     3rd Qu.:2.177e+07                                  
##                     Max.   :1.360e+09                                  
##  GDP_per_Capita     UNDP_Multidim_Poverty_Ratio_.Pop
##  Length:217         Length:217                      
##  Class :character   Class :character                
##  Mode  :character   Mode  :character                
##                                                     
##                                                     
##                                                     
##  Poverty_Ratio_SocialLine_.Pop Population_Rural  
##  Length:217                    Length:217        
##  Class :character              Class :character  
##  Mode  :character              Mode  :character  
##                                                  
##                                                  
##                                                  
##  Prevalence.of.HIV.total....of.population.ages.15.49.
##  Length:217                                          
##  Class :character                                    
##  Mode  :character                                    
##                                                      
##                                                      
##                                                      
##  Incidence.of.tuberculosis..per.100.000.people..
##  Length:217                                     
##  Class :character                               
##  Mode  :character                               
##                                                 
##                                                 
##                                                 
##  Diabetes.prevalence....of.population.ages.20.to.79. Hospital_beds_1000ppl
##  Length:217                                          Length:217           
##  Class :character                                    Class :character     
##  Mode  :character                                    Mode  :character     
##                                                                           
##                                                                           
##                                                                           
##  Physicians_1000ppl Death.rate..crude..per.1.000.people...SP.DYN.CDRT.IN.
##  Length:217         Min.   : 1.266                                       
##  Class :character   1st Qu.: 5.988                                       
##  Mode  :character   Median : 7.739                                       
##                     Mean   : 7.992                                       
##                     3rd Qu.: 9.760                                       
##                     Max.   :16.861                                       
##  Rural.land.area..sq..km...AG.LND.TOTL.RU.K2.
##  Length:217                                  
##  Class :character                            
##  Mode  :character                            
##                                              
##                                              
##                                              
##  Surface.area..sq..km...AG.SRF.TOTL.K2.
##  Length:217                            
##  Class :character                      
##  Mode  :character                      
##                                        
##                                        
## 
# Replace the .. with NA

data_some[data_some == '..'] <- NA
data_some
##                            Country Population_Total
## 1                      Afghanistan     3.183388e+07
## 2                          Albania     2.902012e+06
## 3                          Algeria     3.872355e+07
## 4                   American Samoa     5.328610e+04
## 5                          Andorra     7.716143e+04
## 6                           Angola     2.658364e+07
## 7              Antigua and Barbuda     8.684929e+04
## 8                        Argentina     4.242417e+07
## 9                          Armenia     3.038367e+06
## 10                           Aruba     1.039730e+05
## 11                       Australia     2.307926e+07
## 12                         Austria     8.566906e+06
## 13                      Azerbaijan     9.330278e+06
## 14                    Bahamas, The     3.742072e+05
## 15                         Bahrain     1.253301e+06
## 16                      Bangladesh     1.563961e+08
## 17                        Barbados     2.763531e+05
## 18                         Belarus     9.476676e+06
## 19                         Belgium     1.108831e+07
## 20                          Belize     3.404262e+05
## 21                           Benin     1.083207e+07
## 22                         Bermuda     6.359400e+04
## 23                          Bhutan     7.212714e+05
## 24                         Bolivia     1.066967e+07
## 25          Bosnia and Herzegovina     3.657428e+06
## 26                        Botswana     2.128600e+06
## 27                          Brazil     1.977563e+08
## 28          British Virgin Islands     3.084833e+04
## 29               Brunei Darussalam     4.075456e+05
## 30                        Bulgaria     7.240839e+06
## 31                    Burkina Faso     1.785832e+07
## 32                         Burundi     1.030133e+07
## 33                      Cabo Verde     5.061323e+05
## 34                        Cambodia     1.518629e+07
## 35                        Cameroon     2.176649e+07
## 36                          Canada     3.524247e+07
## 37                  Cayman Islands     5.854210e+04
## 38        Central African Republic     4.656981e+06
## 39                            Chad     1.390984e+07
## 40                 Channel Islands     1.606329e+05
## 41                           Chile     1.781892e+07
## 42                           China     1.360274e+09
## 43                        Colombia     4.639926e+07
## 44                         Comoros     7.017382e+05
## 45                Congo, Dem. Rep.     7.763187e+07
## 46                     Congo, Rep.     4.819028e+06
## 47                      Costa Rica     4.685320e+06
## 48                   Cote d'Ivoire     2.452501e+07
## 49                         Croatia     4.151096e+06
## 50                            Cuba     1.123552e+07
## 51                         Curacao     1.496609e+05
## 52                          Cyprus     1.179559e+06
## 53                         Czechia     1.048752e+07
## 54                         Denmark     5.639088e+06
## 55                        Djibouti     9.826826e+05
## 56                        Dominica     6.862286e+04
## 57              Dominican Republic     1.017830e+07
## 58                         Ecuador     1.578160e+07
## 59                Egypt, Arab Rep.     9.574699e+07
## 60                     El Salvador     6.133012e+06
## 61               Equatorial Guinea     1.349983e+06
## 62                         Eritrea     3.018541e+06
## 63                         Estonia     1.335158e+06
## 64                        Eswatini     1.136427e+06
## 65                        Ethiopia     9.945060e+07
## 66                   Faroe Islands     4.984052e+04
## 67                            Fiji     9.075215e+05
## 68                         Finland     5.413875e+06
## 69                          France     6.570100e+07
## 70                French Polynesia     2.722525e+05
## 71                           Gabon     1.915904e+06
## 72                     Gambia, The     2.113865e+06
## 73                         Georgia     3.780039e+06
## 74                         Germany     8.220985e+07
## 75                           Ghana     2.744969e+07
## 76                       Gibraltar     3.274762e+04
## 77                          Greece     1.086521e+07
## 78                       Greenland     5.653524e+04
## 79                         Grenada     1.132736e+05
## 80                            Guam     1.647058e+05
## 81                       Guatemala     1.536801e+07
## 82                          Guinea     1.135579e+07
## 83                   Guinea-Bissau     1.709993e+06
## 84                          Guyana     7.721852e+05
## 85                           Haiti     1.022689e+07
## 86                        Honduras     8.887120e+06
## 87            Hong Kong SAR, China     7.163986e+06
## 88                         Hungary     9.897702e+06
## 89                         Iceland     3.321802e+05
## 90                           India     1.289124e+09
## 91                       Indonesia     2.547262e+08
## 92              Iran, Islamic Rep.     8.042158e+07
## 93                            Iraq     3.517508e+07
## 94                         Ireland     4.643031e+06
## 95                     Isle of Man     8.277552e+04
## 96                          Israel     8.119471e+06
## 97                           Italy     5.930080e+07
## 98                         Jamaica     2.770056e+06
## 99                           Japan     1.271280e+08
## 100                         Jordan     8.558780e+06
## 101                     Kazakhstan     1.773272e+07
## 102                          Kenya     4.470787e+07
## 103                       Kiribati     1.133770e+05
## 104      Korea, Dem. People's Rep.     2.532425e+07
## 105                    Korea, Rep.     5.021169e+07
## 106                         Kosovo     1.797406e+06
## 107                         Kuwait     3.446383e+06
## 108                Kyrgyz Republic     5.907793e+06
## 109                        Lao PDR     6.641498e+06
## 110                         Latvia     2.047030e+06
## 111                        Lebanon     5.449768e+06
## 112                        Lesotho     2.086487e+06
## 113                        Liberia     4.367451e+06
## 114                          Libya     6.479366e+06
## 115                  Liechtenstein     3.692490e+04
## 116                      Lithuania     3.022694e+06
## 117                     Luxembourg     5.493234e+05
## 118               Macao SAR, China     5.873381e+05
## 119                     Madagascar     2.428609e+07
## 120                         Malawi     1.632120e+07
## 121                       Malaysia     3.014086e+07
## 122                       Maldives     4.057633e+05
## 123                           Mali     1.776445e+07
## 124                          Malta     4.487680e+05
## 125               Marshall Islands     4.831267e+04
## 126                     Mauritania     3.789597e+06
## 127                      Mauritius     1.251696e+06
## 128                         Mexico     1.173933e+08
## 129          Micronesia, Fed. Sts.     1.098817e+05
## 130                        Moldova     2.783877e+06
## 131                         Monaco     3.527705e+04
## 132                       Mongolia     2.934363e+06
## 133                     Montenegro     6.187698e+05
## 134                        Morocco     3.371016e+07
## 135                     Mozambique     2.558981e+07
## 136                        Myanmar     5.033565e+07
## 137                        Namibia     2.329369e+06
## 138                          Nauru     1.073495e+04
## 139                          Nepal     2.767959e+07
## 140                    Netherlands     1.688068e+07
## 141                  New Caledonia     2.691776e+05
## 142                    New Zealand     4.565690e+06
## 143                      Nicaragua     5.991892e+06
## 144                          Niger     1.887536e+07
## 145                        Nigeria     1.814662e+08
## 146                North Macedonia     1.925762e+06
## 147       Northern Mariana Islands     5.356476e+04
## 148                         Norway     5.045191e+06
## 149                           Oman     3.616521e+06
## 150                       Pakistan     2.086671e+08
## 151                          Palau     1.836024e+04
## 152                         Panama     3.829469e+06
## 153               Papua New Guinea     8.285595e+06
## 154                       Paraguay     6.028370e+06
## 155                           Peru     3.029477e+07
## 156                    Philippines     1.010424e+08
## 157                         Poland     3.788447e+07
## 158                       Portugal     1.044604e+07
## 159                    Puerto Rico     3.537562e+06
## 160                          Qatar     1.894866e+06
## 161                        Romania     2.010098e+07
## 162             Russian Federation     1.440363e+08
## 163                         Rwanda     1.116207e+07
## 164                          Samoa     1.992849e+05
## 165                     San Marino     3.256948e+04
## 166          Sao Tome and Principe     1.922880e+05
## 167                   Saudi Arabia     2.672886e+07
## 168                        Senegal     1.402219e+07
## 169                         Serbia     7.142856e+06
## 170                     Seychelles     9.307238e+04
## 171                   Sierra Leone     6.750671e+06
## 172                      Singapore     5.171274e+06
## 173      Sint Maarten (Dutch part)     3.641995e+04
## 174                Slovak Republic     5.410501e+06
## 175                       Slovenia     2.055237e+06
## 176                Solomon Islands     6.107987e+05
## 177                        Somalia     1.355602e+07
## 178                   South Africa     5.510582e+07
## 179                    South Sudan     9.816974e+06
## 180                          Spain     4.610670e+07
## 181                      Sri Lanka     2.106171e+07
## 182            St. Kitts and Nevis     4.681662e+04
## 183                      St. Lucia     1.726166e+05
## 184       St. Martin (French part)     3.465767e+04
## 185 St. Vincent and the Grenadines     1.078582e+05
## 186                          Sudan     3.894343e+07
## 187                       Suriname     5.685874e+05
## 188                         Sweden     9.685628e+06
## 189                    Switzerland     8.086168e+06
## 190           Syrian Arab Republic     2.074978e+07
## 191                     Tajikistan     8.348036e+06
## 192                       Tanzania     5.013794e+07
## 193                       Thailand     6.929041e+07
## 194                    Timor-Leste     1.153769e+06
## 195                           Togo     7.331587e+06
## 196                          Tonga     1.060747e+05
## 197            Trinidad and Tobago     1.338867e+06
## 198                        Tunisia     1.114840e+07
## 199                        Turkiye     7.640729e+07
## 200                   Turkmenistan     6.014850e+06
## 201       Turks and Caicos Islands     3.422029e+04
## 202                         Tuvalu     1.044186e+04
## 203                         Uganda     3.624449e+07
## 204                        Ukraine     4.559534e+07
## 205           United Arab Emirates     7.573282e+06
## 206                 United Kingdom     6.405129e+07
## 207                  United States     3.149526e+08
## 208                        Uruguay     3.344364e+06
## 209                     Uzbekistan     3.006367e+07
## 210                        Vanuatu     2.567376e+05
## 211                  Venezuela, RB     2.866430e+07
## 212                       Viet Nam     9.040825e+07
## 213          Virgin Islands (U.S.)     1.075409e+05
## 214             West Bank and Gaza     4.098806e+06
## 215                    Yemen, Rep.     2.971416e+07
## 216                         Zambia     1.555431e+07
## 217                       Zimbabwe     1.406984e+07
##     Current_Health_Expenditure_.GDP GDP_per_Capita
## 1                        11.2426364    451.4874436
## 2                       6.305844005    4493.153886
## 3                       5.268283782    4545.498223
## 4                              <NA>    11705.50962
## 5                       6.650699064    42338.98155
## 6                       2.918620436    2920.830059
## 7                       5.110502896    16494.48587
## 8                       9.141930304    10186.08225
## 9                       8.956842105    3613.567524
## 10                             <NA>    26699.88887
## 11                      9.099192092    51793.94662
## 12                      10.23421053    47081.92499
## 13                      3.322631579    4964.763133
## 14                      5.372967545    29374.01475
## 15                      4.041130895      23586.559
## 16                      2.232583046    1284.247251
## 17                      6.910125054      18242.059
## 18                      5.710526316    5873.224399
## 19                      10.23358792     44523.6094
## 20                       4.41062335    5861.354914
## 21                      2.874702315    1048.749687
## 22                             <NA>    103047.3282
## 23                      3.781448113    2552.163178
## 24                      5.837991112    2463.695863
## 25                             9.19    4997.859457
## 26                      6.033725739    6415.647824
## 27                      8.618495314    8827.379512
## 28                             <NA>           <NA>
## 29                      2.195953946    33617.41147
## 30                      7.205789474    7940.040173
## 31                      5.061279397    666.1661779
## 32                      8.268308865    205.7231497
## 33                      4.794364164    3540.203208
## 34                      6.688282038    1368.061431
## 35                      3.696850376    1448.111371
## 36                      10.47599082    45454.25459
## 37                             <NA>    82848.08103
## 38                      5.556998728    420.9850319
## 39                      4.606336832    728.1109852
## 40                             <NA>     60868.8135
## 41                      7.783749199    12636.47683
## 42                      4.624752959    6716.493899
## 43                      7.120626424    5879.763607
## 44                      5.429377581    1402.927117
## 45                      3.819649972    398.8776238
## 46                       2.28647202    2497.356884
## 47                      7.403352212     9773.96168
## 48                      4.097755419    1766.442639
## 49                      7.132105263    14152.42821
## 50                      10.75198322     6312.32364
## 51                             <NA>    18848.13906
## 52                      6.574736842    29014.88979
## 53                      7.306246758    20491.14397
## 54                            9.984    58343.35722
## 55                      3.219433508    1928.800896
## 56                      5.298560495    7265.270601
## 57                       4.58832904    6261.184773
## 58                      6.694435119    5044.337852
## 59                      4.691824813    2556.298077
## 60                      8.708990499    3561.302153
## 61                      2.067721851    10335.47608
## 62                      5.106465929    497.5959148
## 63                      6.280223394    18755.84175
## 64                      7.456964293    3500.341109
## 65                      4.125796168    543.7697356
## 66                             <NA>     53369.6723
## 67                      3.561239845    4493.671401
## 68                             9.05    46712.24144
## 69                      11.08443275    39864.08938
## 70                             <NA>    21931.18675
## 71                      2.933351829    7692.224918
## 72                      4.132538331    690.0482551
## 73                            7.933    3943.010288
## 74                      11.12894034    44291.54316
## 75                      3.916908603    1579.339022
## 76                             <NA>           <NA>
## 77                      8.623236807     22332.7167
## 78                             <NA>    44988.35503
## 79                      5.206825106    8081.951869
## 80                             <NA>     31953.8835
## 81                      6.169551724    3510.418896
## 82                      4.057360837    814.2916512
## 83                      6.434563135    667.2952505
## 84                      4.196538121    6167.413372
## 85                      4.102177909    1262.361463
## 86                      8.015170374    2084.924914
## 87                             <NA>    38294.74006
## 88                      7.256842105      14367.019
## 89                           8.6245    58253.49624
## 90                      3.452631579    1487.062035
## 91                      2.829826406    3082.968832
## 92                      6.082438895    4870.444621
## 93                      3.599363903     4441.51616
## 94                      8.169644046    66564.53919
## 95                             <NA>    70635.62348
## 96                      7.039473684     35610.8813
## 97                            8.707     34733.2027
## 98                      5.407712334     5001.91238
## 99                      9.601578947    39424.89045
## 100                     7.773431176    3574.815553
## 101                     3.213157895    8632.755891
## 102                     4.730462288    1324.684593
## 103                     11.51789474    1629.494409
## 104                            <NA>           <NA>
## 105                      6.38518803     26395.0069
## 106                            <NA>    4008.653856
## 107                     3.396623813    37437.46975
## 108                     6.642631579     1072.10284
## 109                     2.752456187    1589.848155
## 110                     6.070526316    13954.98143
## 111                     7.948515442    6588.222944
## 112                     8.838850373    1004.059832
## 113                     9.020622479    560.9782408
## 114                     3.079954493    8956.304324
## 115                            <NA>    150101.8561
## 116                            6.52    15406.66863
## 117                           5.931    109231.9904
## 118                            <NA>    54881.66527
## 119                     4.455707863    449.5098564
## 120                     6.106377928    540.7598065
## 121                     3.446505697    9052.014017
## 122                     8.786842105     8098.60639
## 123                     4.440895093     694.946244
## 124                     8.703157895    25691.87858
## 125                     14.86154471    4006.491575
## 126                     3.211177388    1594.251035
## 127                     4.710111667    8724.106087
## 128                     5.737095081    9992.807482
## 129                     12.13124807    2945.613811
## 130                     8.874210526    3052.428947
## 131                     4.333218199    177869.5426
## 132                     4.320910391    3204.194369
## 133                     8.819090909    7052.070075
## 134                     5.038204319    3099.450184
## 135                      5.87018835    533.2574143
## 136                     3.420526316     965.586106
## 137                     9.459853022    4554.559087
## 138                     12.32979865    7677.608928
## 139                     4.474736842    791.5462904
## 140                     9.994736842    51275.65537
## 141                            <NA>    32639.73246
## 142                     9.119852894    37561.12662
## 143                     7.607375998    1764.675457
## 144                     5.214959746    487.9278815
## 145                     3.729135237    2008.130896
## 146                     7.051578947    5403.221187
## 147                            <NA>    18822.71773
## 148                          9.2235    83085.93364
## 149                     3.025274057    19709.47279
## 150                     2.573061365    1187.556146
## 151                     11.46376545    12639.55519
## 152                      7.55064239    11310.53566
## 153                     2.409026196    2063.660702
## 154                     6.072227704    4865.547919
## 155                     4.910122168    5458.101783
## 156                          4.0895    2508.895892
## 157                          6.3085    13330.87094
## 158                          9.7005    21907.97357
## 159                            <NA>    28145.35544
## 160                     2.664236132    71861.00433
## 161                     5.305263158    9854.693726
## 162                     5.348947368    10578.62365
## 163                     7.303107914    642.6923054
## 164                     5.317211703    3597.543938
## 165                     7.049094476     54233.0566
## 166                     7.683368382    1449.696875
## 167                     4.625313283    23320.19909
## 168                     4.188314012     1306.71208
## 169                     8.864323339    6757.944275
## 170                     4.677906664    14028.06898
## 171                     10.65651126    754.0572643
## 172                     3.794145396    53595.50361
## 173                            <NA>    32449.08868
## 174                     7.016842105    17283.66745
## 175                          8.4255    23605.40865
## 176                     5.568360793    1792.165577
## 177                            <NA>    434.5345085
## 178                     7.706607241    6477.800354
## 179                     10.07183256    1375.877318
## 180                     8.864402218    29285.69457
## 181                     3.747368421    3014.031811
## 182                     5.116900721    17946.60954
## 183                     5.016750134    9510.273258
## 184                            <NA>    20567.69883
## 185                     4.192395736    7181.386048
## 186                     5.042841596    1085.614915
## 187                     5.464989663    6245.639271
## 188                           9.821    52574.84705
## 189                     10.43189089    78503.01779
## 190                     3.700341392    1457.382506
## 191                     6.247368421    762.7129909
## 192                     4.868653122    846.6287275
## 193                      3.65016665    5343.791368
## 194                      6.83673939    1137.380449
## 195                     4.729092272    754.9695488
## 196                     4.750224276    3731.753657
## 197                     5.323858072    17296.01771
## 198                     5.897287995    3804.384555
## 199                     4.683157895    9879.268667
## 200                     4.680823804    5001.805236
## 201                            <NA>    24992.06601
## 202                     15.19517291    3759.338954
## 203                     5.272838818    702.4227647
## 204                     6.918947368    3125.154936
## 205                     3.589794799    44112.01078
## 206                     9.763402509    43234.43063
## 207                      16.0682107    55979.98896
## 208                     8.410401746     14018.9084
## 209                     5.218947368    1795.468185
## 210                     3.541446108    2751.688233
## 211                     3.181720378    9716.628345
## 212                     4.632116643    2247.165631
## 213                            <NA>    38101.09984
## 214                            <NA>    2789.594057
## 215                     5.288947912    887.0500411
## 216                     5.058898525     1252.39767
## 217                     6.303795893    1289.531294
##     UNDP_Multidim_Poverty_Ratio_.Pop Poverty_Ratio_SocialLine_.Pop
## 1                               55.9                          <NA>
## 2                                0.7                         19.14
## 3                                1.4                          17.6
## 4                               <NA>                          <NA>
## 5                               <NA>                          <NA>
## 6                               51.1                          38.8
## 7                               <NA>                          <NA>
## 8                                0.4                   25.51052632
## 9                                0.2                         21.78
## 10                              <NA>                          <NA>
## 11                              <NA>                   11.88571429
## 12                              <NA>                   11.41578947
## 13                              <NA>                   10.83333333
## 14                              <NA>                          <NA>
## 15                              <NA>                          <NA>
## 16                              24.6                        34.825
## 17                               2.5                          <NA>
## 18                              <NA>                   13.68888889
## 19                              <NA>                   10.57368421
## 20                               4.3                          <NA>
## 21                              66.8                          46.4
## 22                              <NA>                          <NA>
## 23                              <NA>                         26.04
## 24                               9.1                   27.77058824
## 25                               2.2                          16.4
## 26                              17.2                         37.15
## 27                               3.8                   29.66315789
## 28                              <NA>                          <NA>
## 29                              <NA>                          <NA>
## 30                              <NA>                          18.5
## 31                              <NA>                         47.88
## 32                              75.1                   66.33333333
## 33                              <NA>                          30.8
## 34                              16.6                          <NA>
## 35                              43.6                          40.3
## 36                              <NA>                   14.62941176
## 37                              <NA>                          <NA>
## 38                              80.4                          63.8
## 39                              84.2                         47.05
## 40                              <NA>                          <NA>
## 41                              <NA>                   21.72222222
## 42                               3.9                   27.74285714
## 43                               4.8                   30.58888889
## 44                              37.3                          35.2
## 45                              64.5                   80.03333333
## 46                              24.3                         47.55
## 47                               0.5                   24.85714286
## 48                              46.1                         38.85
## 49                              <NA>                   16.41538462
## 50                               0.7                          <NA>
## 51                              <NA>                          <NA>
## 52                              <NA>                   9.511111111
## 53                              <NA>                   7.955555556
## 54                              <NA>                   7.194736842
## 55                              <NA>                          36.7
## 56                              <NA>                          <NA>
## 57                               2.3                         26.74
## 58                               2.1                   28.74285714
## 59                               5.2                   21.55714286
## 60                               7.9                   27.58947368
## 61                              <NA>                          <NA>
## 62                              <NA>                          <NA>
## 63                              <NA>                   13.16842105
## 64                              19.2                          47.9
## 65                              68.7                   42.83333333
## 66                              <NA>                          <NA>
## 67                               1.5                   21.06666667
## 68                              <NA>                   7.836842105
## 69                              <NA>                   11.64210526
## 70                              <NA>                          <NA>
## 71                              15.6                         26.75
## 72                              41.7                         40.95
## 73                               0.3                        30.235
## 74                              <NA>                   10.93888889
## 75                              24.6                   42.23333333
## 76                              <NA>                          <NA>
## 77                              <NA>                   17.74736842
## 78                              <NA>                          <NA>
## 79                              <NA>                          18.8
## 80                              <NA>                          <NA>
## 81                              28.9                            33
## 82                              66.2                   41.73333333
## 83                              64.4                   48.86666667
## 84                               1.8                          <NA>
## 85                              41.3                          41.8
## 86                                12                   35.91764706
## 87                              <NA>                          <NA>
## 88                              <NA>                   14.61111111
## 89                              <NA>                   7.593333333
## 90                              16.4                         37.51
## 91                               3.6                   33.05714286
## 92                              <NA>                         21.06
## 93                               8.6                         15.75
## 94                              <NA>                   11.34736842
## 95                              <NA>                          <NA>
## 96                              <NA>                   23.98947368
## 97                              <NA>                   16.28421053
## 98                               2.8                   20.73333333
## 99                              <NA>                          12.9
## 100                              0.4                   14.36666667
## 101                              0.5                   15.22105263
## 102                             37.5                        44.625
## 103                             19.8                          27.4
## 104                             <NA>                          <NA>
## 105                             <NA>                   15.24545455
## 106                             <NA>                         20.35
## 107                             <NA>                          <NA>
## 108                              0.4                         24.65
## 109                             23.1                   33.43333333
## 110                             <NA>                   16.88333333
## 111                             <NA>                          13.2
## 112                             19.6                          43.1
## 113                             52.3                   44.96666667
## 114                                2                          <NA>
## 115                             <NA>                          <NA>
## 116                             <NA>                   16.84444444
## 117                             <NA>                   12.83157895
## 118                             <NA>                          <NA>
## 119                             68.4                   78.36666667
## 120                             49.9                        68.275
## 121                             <NA>                          22.9
## 122                              0.8                   17.96666667
## 123                             68.3                          44.5
## 124                             <NA>                   11.69333333
## 125                             <NA>                          20.3
## 126                             58.4                          31.9
## 127                             <NA>                   17.76666667
## 128                              4.1                   26.89090909
## 129                             <NA>                          33.8
## 130                              0.9                   18.17222222
## 131                             <NA>                          <NA>
## 132                              7.3                       21.5125
## 133                              1.2                         24.32
## 134                              6.4                         25.05
## 135                             61.9                   69.96666667
## 136                             38.3                         25.85
## 137                             40.9                            41
## 138                             <NA>                          24.9
## 139                             17.5                          33.2
## 140                             <NA>                           9.3
## 141                             <NA>                          <NA>
## 142                             <NA>                          <NA>
## 143                             16.5                          30.8
## 144                               91                   63.41666667
## 145                               33                         45.16
## 146                              0.4                   26.05454545
## 147                             <NA>                          <NA>
## 148                             <NA>                   8.311764706
## 149                             <NA>                          <NA>
## 150                             38.3                       32.7875
## 151                             <NA>                          <NA>
## 152                             <NA>                   27.96315789
## 153                             56.6                          46.9
## 154                              4.5                         26.99
## 155                              6.6                        29.825
## 156                              5.8                   32.08571429
## 157                             <NA>                   15.54444444
## 158                             <NA>                   14.76315789
## 159                             <NA>                          <NA>
## 160                             <NA>                          17.5
## 161                             <NA>                      24.69375
## 162                             <NA>                          17.2
## 163                             48.8                         58.15
## 164                              6.3                         23.95
## 165                             <NA>                          <NA>
## 166                             11.7                          37.3
## 167                             <NA>                          <NA>
## 168                             50.8                        39.775
## 169                              0.1                         23.07
## 170                              0.9                          17.2
## 171                             59.2                   49.83333333
## 172                             <NA>                          <NA>
## 173                             <NA>                          <NA>
## 174                             <NA>                   12.17222222
## 175                             <NA>                   8.127777778
## 176                             <NA>                         46.25
## 177                             <NA>                          <NA>
## 178                              6.3                        39.075
## 179                             <NA>                          55.8
## 180                             <NA>                   17.29473684
## 181                              2.9                         25.36
## 182                             <NA>                          <NA>
## 183                              1.9                          19.4
## 184                             <NA>                          <NA>
## 185                             <NA>                          <NA>
## 186                             52.3                          35.9
## 187                              2.9                          21.7
## 188                             <NA>                   10.42631579
## 189                             <NA>                      11.83125
## 190                             <NA>                        29.625
## 191                              7.4                         34.72
## 192                             57.1                   51.83333333
## 193                              0.6                   20.65882353
## 194                             48.3                          44.2
## 195                             37.6                         51.28
## 196                              0.9                   20.83333333
## 197                              0.6                          <NA>
## 198                              0.8                        20.575
## 199                             <NA>                         21.94
## 200                              0.2                          <NA>
## 201                             <NA>                          <NA>
## 202                              2.1                            27
## 203                             57.2                            50
## 204                              0.2                   13.00555556
## 205                             <NA>                         16.25
## 206                             <NA>                   13.89473684
## 207                             <NA>                         19.14
## 208                             <NA>                        23.065
## 209                              1.7                          50.2
## 210                             <NA>                         33.25
## 211                             <NA>                        33.075
## 212                              1.9                         25.82
## 213                             <NA>                          <NA>
## 214                              0.6                       18.9625
## 215                             48.5                         34.45
## 216                             47.9                         63.06
## 217                             25.8                            43
##     Population_Rural Prevalence.of.HIV.total....of.population.ages.15.49.
## 1        23970533.76                                                  0.1
## 2        1308266.095                                                  0.1
## 3        11725015.71                                                  0.1
## 4        6643.761905                                                 <NA>
## 5        8656.571429                                                 <NA>
## 6        9902981.571                                                1.745
## 7        64215.28571                                                 <NA>
## 8        3721265.857                                                 0.39
## 9        1109743.905                                                 0.17
## 10       58498.09524                                                 <NA>
## 11       3335954.333                                                  0.1
## 12       3575886.667                                                 <NA>
## 13       4244327.667                                                  0.1
## 14       64618.66667                                                 1.39
## 15       137562.2857                                                 <NA>
## 16       104651411.4                                                  0.1
## 17       188545.8095                                                 1.07
## 18       2263897.381                                                 0.28
## 19        245864.381                                                 0.19
## 20       185189.8571                                                 1.37
## 21       5928815.381                                                 1.14
## 22                 0                                                 <NA>
## 23       452387.1429                                                  0.2
## 24       3448099.333                                                0.315
## 25       1959002.429                                                 <NA>
## 26       742751.0476                                                21.19
## 27        29241718.1                                                0.485
## 28       16551.71429                                                 <NA>
## 29       97432.71429                                                 <NA>
## 30       1939507.714                                                  0.1
## 31       13033995.48                                                1.015
## 32       9068831.476                                                 1.51
## 33       187728.2857                                                 0.83
## 34       11866979.38                                                0.805
## 35       10017097.76                                                3.885
## 36       6678381.238                                                  0.2
## 37                 0                                                 <NA>
## 38       2786369.048                                                 4.61
## 39       10749496.38                                                 1.46
## 40       110929.1905                                                 <NA>
## 41       2262842.333                                                0.375
## 42       639794213.2                                                 <NA>
## 43       9690497.524                                                0.415
## 44       501018.4286                                                  0.1
## 45       44703671.81                                                 0.99
## 46       1681629.238                                                 3.69
## 47       1193027.286                                                 0.35
## 48       12496495.76                                                 3.29
## 49       1831440.238                                                  0.1
## 50       2612732.048                                                 0.39
## 51       15477.14286                                                 <NA>
## 52       385279.3333                                                  0.1
## 53       2763000.952                                                  0.1
## 54       723705.4286                                                  0.1
## 55            221745                                                 <NA>
## 56       21287.42857                                                 <NA>
## 57       2406635.905                                                 1.13
## 58       5813075.381                                                 0.37
## 59       54663154.67                                                  0.1
## 60       1962566.857                                                0.515
## 61       424576.7143                                                6.015
## 62       1897286.476                                                  0.8
## 63       418074.7619                                                0.705
## 64       873650.9048                                                27.91
## 65       80382410.52                                                 1.33
## 66       29269.66667                                                 <NA>
## 67       419334.1905                                                0.155
## 68       839968.2381                                                 <NA>
## 69       13662287.43                                                 0.34
## 70       107536.7143                                                 <NA>
## 71       243538.9524                                                    4
## 72       879443.0952                                                1.805
## 73       1637912.762                                                0.195
## 74       18959086.95                                                  0.1
## 75       12832511.57                                                1.925
## 76                 0                                                 <NA>
## 77       2470947.524                                                0.155
## 78              8313                                                 <NA>
## 79       72341.38095                                                 <NA>
## 80              9323                                                 <NA>
## 81       7731157.238                                                0.295
## 82       7387051.381                                                1.635
## 83       996546.7143                                                3.565
## 84       564070.5238                                                 1.38
## 85       5024909.476                                                1.765
## 86       4062541.524                                                 0.39
## 87                 0                                                 <NA>
## 88       3012544.476                                                 <NA>
## 89       21280.42857                                                  0.1
## 90       871947246.9                                                0.305
## 91       122094823.1                                                0.315
## 92       22222793.43                                                  0.1
## 93       10577102.62                                                  0.1
## 94       1752304.762                                          0.163157895
## 95       39456.57143                                                 <NA>
## 96       639919.6667                                                 <NA>
## 97       18194522.38                                                0.255
## 98       1257545.714                                                 1.37
## 99       12782392.14                                                 <NA>
## 100      1020082.143                                                  0.1
## 101       7600387.19                                                 <NA>
## 102       33378277.9                                                5.305
## 103      56149.38095                                                 <NA>
## 104      9850210.667                                                 <NA>
## 105      9271045.048                                                 <NA>
## 106             <NA>                                                 <NA>
## 107                0                                                  0.1
## 108      3780829.024                                                0.155
## 109      4499112.286                                                0.285
## 110      654232.5238                                                0.495
## 111           658810                                                  0.1
## 112      1541649.381                                               23.095
## 113          2202226                                                1.475
## 114      1359240.524                                                0.135
## 115      31575.14286                                                 <NA>
## 116      990221.7143                                                0.145
## 117      57378.28571                                                  0.2
## 118                0                                                 <NA>
## 119      15880585.38                                                 0.21
## 120      13649663.71                                                10.04
## 121      8155564.857                                                0.385
## 122      251392.4762                                                  0.1
## 123      10786790.33                                                1.225
## 124      25668.19048                                                0.155
## 125      12290.47619                                                 <NA>
## 126      1889007.952                                                0.435
## 127      734296.0476                                                 <NA>
## 128      24882013.57                                                 0.35
## 129      85096.09524                                                 <NA>
## 130      1593555.762                                                0.645
## 131                0                                                 <NA>
## 132       968801.381                                                  0.1
## 133      215710.8571                                                  0.1
## 134      13511740.57                                                  0.1
## 135      16867680.33                                                11.71
## 136      35410101.57                                                 0.87
## 137      1266037.905                                                12.87
## 138                0                                                 <NA>
## 139      22682431.05                                                 0.17
## 140      1968441.048                                          0.189473684
## 141       85044.2381                                                 <NA>
## 142      619458.1429                                                  0.1
## 143      2536489.048                                                0.265
## 144      15776179.43                                                0.355
## 145      96553618.29                                                 <NA>
## 146           814017                                                  0.1
## 147      4736.952381                                                 <NA>
## 148      982412.0476                                                 <NA>
## 149      695857.5714                                                  0.1
## 150      133931919.8                                                 0.12
## 151      4332.666667                                                 <NA>
## 152      1288529.286                                                0.885
## 153      7193802.048                                                 0.77
## 154      2396036.952                                                 0.34
## 155      6979358.476                                                 0.37
## 156       54163801.9                                                 0.13
## 157      14904158.24                                                  0.1
## 158      3944734.857                                                0.585
## 159      219506.5714                                                 <NA>
## 160      22695.85714                                                  0.1
## 161      9285937.524                                                  0.1
## 162      37360822.33                                                 <NA>
## 163       9247334.19                                                 3.38
## 164      160686.7143                                                 <NA>
## 165      1222.238095                                                 <NA>
## 166       60974.7619                                                 0.96
## 167      4554874.952                                                  0.1
## 168      7639261.143                                                0.505
## 169      3192188.714                                                  0.1
## 170      41945.57143                                                 <NA>
## 171      4019073.238                                                 1.58
## 172                0                                                 <NA>
## 173                0                                                 <NA>
## 174      2463718.143                                                  0.1
## 175      956938.2857                                                  0.1
## 176      476740.9524                                                 <NA>
## 177      7845793.905                                                 <NA>
## 178      19841596.29                                                17.58
## 179      7971479.714                                                 2.28
## 180      9594185.619                                                0.305
## 181      17180006.57                                                  0.1
## 182      32183.71429                                                 <NA>
## 183       138475.619                                                 <NA>
## 184             <NA>                                                 <NA>
## 185       53796.7619                                                 <NA>
## 186      25675997.33                                                  0.1
## 187      191603.1905                                                 1.46
## 188      1335513.952                                                 <NA>
## 189      2125101.381                                                  0.2
## 190      9470445.714                                                  0.1
## 191      6096993.476                                                0.135
## 192         34562357                                                5.425
## 193      37686173.38                                                 1.55
## 194           818006                                                0.135
## 195      4423771.238                                                  2.6
## 196       81435.2381                                                 <NA>
## 197      617704.6667                                                1.285
## 198      3607941.667                                                  0.1
## 199       20945003.9                                                 <NA>
## 200      3000828.143                                                 <NA>
## 201      2868.380952                                                 <NA>
## 202      4431.857143                                                 <NA>
## 203      28365603.48                                                 6.07
## 204      14228444.71                                                 <NA>
## 205      1106759.762                                                  0.1
## 206      11446102.52                                                 <NA>
## 207      58560037.67                                                  0.4
## 208      179461.1905                                                 0.49
## 209       14974166.1                                                 <NA>
## 210      193327.5714                                                 <NA>
## 211       3400355.81                                                0.525
## 212      60699087.57                                                 0.36
## 213      5445.142857                                                 <NA>
## 214      1017335.381                                                 <NA>
## 215      19513585.71                                                  0.1
## 216      9087849.429                                                12.95
## 217      9438587.714                                               15.025
##     Incidence.of.tuberculosis..per.100.000.people..
## 1                                       187.8571429
## 2                                       17.19047619
## 3                                       69.47619048
## 4                                       6.771428571
## 5                                       8.157142857
## 6                                       361.3333333
## 7                                       4.452380952
## 8                                       27.71428571
## 9                                       51.61904762
## 10                                              9.3
## 11                                      6.361904762
## 12                                      8.557142857
## 13                                      80.14285714
## 14                                       14.3952381
## 15                                      21.57142857
## 16                                              221
## 17                                      2.012857143
## 18                                      52.23809524
## 19                                      9.704761905
## 20                                       32.0952381
## 21                                      63.23809524
## 22                                      3.757142857
## 23                                      180.7142857
## 24                                       127.952381
## 25                                      44.76190476
## 26                                              453
## 27                                      46.28571429
## 28                                       1.85047619
## 29                                      63.71428571
## 30                                      33.66666667
## 31                                      54.47619048
## 32                                      139.6666667
## 33                                      67.66666667
## 34                                      399.1904762
## 35                                      234.3809524
## 36                                      5.414285714
## 37                                      5.019047619
## 38                                              540
## 39                                      145.5238095
## 40                                             <NA>
## 41                                      16.61904762
## 42                                      71.66666667
## 43                                      34.23809524
## 44                                      34.85714286
## 45                                      323.8571429
## 46                                      388.9047619
## 47                                      12.46190476
## 48                                      186.0952381
## 49                                      16.51428571
## 50                                      7.495238095
## 51                                      3.388571429
## 52                                      4.876190476
## 53                                      6.866666667
## 54                                      6.166666667
## 55                                      366.8095238
## 56                                      11.17142857
## 57                                      52.66666667
## 58                                       44.9047619
## 59                                       15.9047619
## 60                                      46.28571429
## 61                                      240.4761905
## 62                                      140.5238095
## 63                                      24.06666667
## 64                                      935.1904762
## 65                                      229.6666667
## 66                                             <NA>
## 67                                      40.38095238
## 68                                      5.733333333
## 69                                      9.095238095
## 70                                      22.52380952
## 71                                      547.3809524
## 72                                              172
## 73                                      115.6666667
## 74                                      6.352380952
## 75                                      169.3333333
## 76                                             <NA>
## 77                                      5.157142857
## 78                                      150.2857143
## 79                                      3.020952381
## 80                                               46
## 81                                      27.71428571
## 82                                      186.5238095
## 83                                      359.7142857
## 84                                       95.0952381
## 85                                      211.2857143
## 86                                      42.71428571
## 87                                      74.80952381
## 88                                      12.95714286
## 89                                      3.604761905
## 90                                              252
## 91                                      341.9047619
## 92                                      15.57142857
## 93                                      38.28571429
## 94                                              8.5
## 95                                             <NA>
## 96                                      4.866666667
## 97                                      6.595238095
## 98                                      4.314285714
## 99                                      18.03333333
## 100                                             5.8
## 101                                     117.2857143
## 102                                      439.047619
## 103                                     410.1428571
## 104                                             513
## 105                                     76.76190476
## 106                                            <NA>
## 107                                     25.27142857
## 108                                     127.5714286
## 109                                     202.4761905
## 110                                     45.38095238
## 111                                     11.50952381
## 112                                     931.9047619
## 113                                     294.6666667
## 114                                     44.52380952
## 115                                            <NA>
## 116                                     57.52380952
## 117                                     7.528571429
## 118                                     72.66666667
## 119                                             242
## 120                                     261.2857143
## 121                                     85.71428571
## 122                                     39.33333333
## 123                                     59.57142857
## 124                                     11.65238095
## 125                                     424.4761905
## 126                                     124.7619048
## 127                                     12.33333333
## 128                                     22.57142857
## 129                                     145.1904762
## 130                                      109.047619
## 131                                     1.190952381
## 132                                      431.952381
## 133                                     19.21052632
## 134                                     98.33333333
## 135                                     355.6190476
## 136                                             454
## 137                                     792.7619048
## 138                                     97.33333333
## 139                                     290.5714286
## 140                                     6.147619048
## 141                                     18.06190476
## 142                                     7.852380952
## 143                                     48.66666667
## 144                                             107
## 145                                             219
## 146                                     21.52380952
## 147                                     80.19047619
## 148                                     5.976190476
## 149                                     10.95238095
## 150                                     272.6190476
## 151                                     77.38095238
## 152                                     52.76190476
## 153                                             432
## 154                                     44.42857143
## 155                                     135.1428571
## 156                                     552.2380952
## 157                                      19.4047619
## 158                                     26.04761905
## 159                                     1.975238095
## 160                                     36.04761905
## 161                                     93.95238095
## 162                                      70.0952381
## 163                                              76
## 164                                     11.07619048
## 165                                     0.195238095
## 166                                     122.8571429
## 167                                     14.18571429
## 168                                     127.2380952
## 169                                     24.31578947
## 170                                     16.89047619
## 171                                     306.6190476
## 172                                              43
## 173                                     6.607142857
## 174                                             8.5
## 175                                     8.685714286
## 176                                     77.19047619
## 177                                     273.1904762
## 178                                     954.4285714
## 179                                             227
## 180                                     13.86190476
## 181                                      64.9047619
## 182                                      4.40952381
## 183                                     6.642857143
## 184                                            <NA>
## 185                                     10.55714286
## 186                                     99.04761905
## 187                                              31
## 188                                     6.071428571
## 189                                     6.714285714
## 190                                     22.19047619
## 191                                     121.5714286
## 192                                     356.1904762
## 193                                             178
## 194                                             498
## 195                                     56.52380952
## 196                                     12.38095238
## 197                                      18.0952381
## 198                                     32.38095238
## 199                                     22.19047619
## 200                                     66.61904762
## 201                                     18.05238095
## 202                                     219.7142857
## 203                                     210.7142857
## 204                                     102.7619048
## 205                                     1.448571429
## 206                                     11.63333333
## 207                                     3.833333333
## 208                                     28.85714286
## 209                                     87.80952381
## 210                                     61.33333333
## 211                                     35.04761905
## 212                                     216.3809524
## 213                                            <NA>
## 214                                     0.923809524
## 215                                     56.85714286
## 216                                     445.4285714
## 217                                     356.4761905
##     Diabetes.prevalence....of.population.ages.20.to.79. Hospital_beds_1000ppl
## 1                                                  9.25           0.417894737
## 2                                                   6.5           2.953888889
## 3                                                  7.05           1.699230769
## 4                                                  20.3                  <NA>
## 5                                                  7.55                  2.72
## 6                                                  3.75                 0.775
## 7                                                  12.1           2.782666667
## 8                                                  5.45           4.314615385
## 9                                                  7.05           4.359444444
## 10                                                 8.35                  <NA>
## 11                                                  6.5           3.837692308
## 12                                                  5.6           7.556111111
## 13                                                  4.2           5.260588235
## 14                                                10.45           2.885555556
## 15                                                 15.4           1.972941176
## 16                                                12.35           0.617142857
## 17                                                 13.2                6.4875
## 18                                                  6.8           10.21235294
## 19                                                  4.2           5.994210526
## 20                                                15.75           1.119999997
## 21                                                 1.55           0.458461538
## 22                                                 12.5                  <NA>
## 23                                                 8.05           1.777894737
## 24                                                 6.05           1.136842105
## 25                                                  8.3           2.207058824
## 26                                                    8           2.284210526
## 27                                                 9.45           2.428888889
## 28                                                 8.65                  <NA>
## 29                                                10.25           3.055789474
## 30                                                 7.05           6.745555556
## 31                                                 2.55                   0.5
## 32                                                  4.6                  0.79
## 33                                                 3.75           1.989333333
## 34                                                  5.1           0.685555556
## 35                                                  5.8           1.816666667
## 36                                                 8.05           2.826111111
## 37                                                 12.5                  <NA>
## 38                                                  4.5                   1.1
## 39                                                 4.85           0.446666667
## 40                                                  5.6                  <NA>
## 41                                                10.15           2.194210526
## 42                                                  9.7           3.098333333
## 43                                                 9.05           1.485333335
## 44                                                10.05                  2.18
## 45                                                 4.45                  0.95
## 46                                                  5.5                   1.6
## 47                                                  9.2           1.217894737
## 48                                                  3.5                   0.4
## 49                                                    5                  5.59
## 50                                                  8.5           4.390526316
## 51                                                 11.7                  <NA>
## 52                                                 8.95           2.745882353
## 53                                                  6.2                  7.03
## 54                                                 5.45           3.161111111
## 55                                                 6.85           1.406153846
## 56                                                 10.3           3.661666667
## 57                                                  9.3           1.551249994
## 58                                                  5.5           1.473157895
## 59                                                18.75           1.526111111
## 60                                                 7.85           1.029411765
## 61                                                 4.85           2.066666667
## 62                                                 5.05           1.232777778
## 63                                                 6.75           5.132222222
## 64                                                 3.85                  2.08
## 65                                                  4.2                 1.255
## 66                                                  3.8                  <NA>
## 67                                                 14.3           1.982307692
## 68                                                 5.95           5.321666667
## 69                                                 5.35                 6.775
## 70                                                 16.9                  <NA>
## 71                                                  7.9                  2.89
## 72                                                 1.95           1.034444444
## 73                                                 4.25           3.842777778
## 74                                                  6.1           8.248888889
## 75                                                  3.8               0.75125
## 76                                                 <NA>                  <NA>
## 77                                                 5.75           4.514705882
## 78                                                  3.3                  <NA>
## 79                                                 10.6           3.367692293
## 80                                                13.85                  <NA>
## 81                                                 11.2                 0.565
## 82                                                 3.25                   0.3
## 83                                                 2.55                  0.85
## 84                                                 14.2           2.026428571
## 85                                                 7.75                  3.23
## 86                                                  5.9           0.603157895
## 87                                                  7.7                  <NA>
## 88                                                  6.5           7.214210526
## 89                                                 4.35               3.42394
## 90                                                  9.3           1.640555554
## 91                                                 7.85           0.902631579
## 92                                                 10.1           1.710588235
## 93                                                  9.9           1.223684211
## 94                                                  4.1           3.663888889
## 95                                                  6.3           1.646139087
## 96                                                 7.95           3.343157895
## 97                                                 5.75           3.536111111
## 98                                                 13.4           1.727777776
## 99                                                 7.15           13.46055556
## 100                                               13.75           1.581052632
## 101                                                7.15           6.617777778
## 102                                                4.55                1.3825
## 103                                                23.7           1.557142857
## 104                                                 8.5                 13.75
## 105                                                7.15                  9.69
## 106                                                <NA>                  <NA>
## 107                                                22.8           2.285555556
## 108                                                6.45           4.787368421
## 109                                                4.75           1.244736842
## 110                                                6.85           6.442777778
## 111                                                13.8           3.122105263
## 112                                                   4                   1.3
## 113                                                 2.7                 1.072
## 114                                               11.25           3.573684211
## 115                                                 5.4                  <NA>
## 116                                                 6.8                 6.935
## 117                                                 5.2           5.164736842
## 118                                                 7.5                  <NA>
## 119                                                4.65           0.336666667
## 120                                                6.45                   1.2
## 121                                               15.55           1.911052632
## 122                                                 9.2           4.090769231
## 123                                                   2           0.273333333
## 124                                                 7.3           5.439444444
## 125                                                22.4                 2.715
## 126                                                 3.2                   0.4
## 127                                                18.7           3.465789474
## 128                                               16.25           1.025263158
## 129                                               15.65                  3.28
## 130                                                4.15                  5.84
## 131                                                 5.9           17.30444444
## 132                                                   7           7.416315789
## 133                                                 8.4           3.917777778
## 134                                                7.95           0.888823529
## 135                                                 3.2              0.786875
## 136                                                 7.1           0.881666667
## 137                                                7.25                     3
## 138                                                21.9                  3.96
## 139                                                6.15           0.255789474
## 140                                                 4.9           3.829444444
## 141                                                  16                  <NA>
## 142                                                 7.4           2.684615385
## 143                                               10.15           0.893157893
## 144                                                 4.7           0.294545455
## 145                                                 4.2                   0.5
## 146                                                 6.9           4.396111111
## 147                                                23.4                  <NA>
## 148                                                4.15           4.223888889
## 149                                               12.15           1.452222222
## 150                                               19.35           0.474705882
## 151                                               14.05                  5.03
## 152                                                 8.8                 2.085
## 153                                               12.15                  0.17
## 154                                                   7           0.852222222
## 155                                                 5.4            1.50235294
## 156                                                 8.4           1.028947368
## 157                                                 7.9           6.467222222
## 158                                                9.35                 3.435
## 159                                               13.15                  <NA>
## 160                                               19.65                  1.38
## 161                                                 7.1           6.811666667
## 162                                                7.65           8.384736842
## 163                                                 4.8              0.891875
## 164                                                8.45           1.326666667
## 165                                                6.45                 3.415
## 166                                                 5.5                3.0475
## 167                                               19.15           2.084210526
## 168                                                3.15                  0.51
## 169                                                 8.4              5.256875
## 170                                                10.3           3.692222222
## 171                                                2.65                   0.4
## 172                                               10.55           2.428888889
## 173                                                <NA>                  <NA>
## 174                                                5.75           6.226111111
## 175                                                6.65           4.573333333
## 176                                               17.55                 1.665
## 177                                                 5.4                  0.77
## 178                                                 8.9                 2.518
## 179                                                 6.5                  <NA>
## 180                                                 8.3           3.098947368
## 181                                                 9.4           3.578888889
## 182                                                12.3           5.088235294
## 183                                               10.05           2.019090909
## 184                                                <NA>                  <NA>
## 185                                                 8.4           3.574285714
## 186                                               13.75           0.731666667
## 187                                                10.8                 2.948
## 188                                                 4.6           2.594444444
## 189                                                5.25           5.009473684
## 190                                                12.4           1.450526316
## 191                                                6.45           4.978421053
## 192                                                7.55                 0.704
## 193                                                 8.6           2.085263158
## 194                                                8.05                   5.9
## 195                                                 2.7                  0.67
## 196                                               13.85           2.426666667
## 197                                                12.7           2.428823529
## 198                                                9.55           1.821052632
## 199                                                11.2           2.646666667
## 200                                                 4.7           4.158947368
## 201                                                <NA>                  <NA>
## 202                                               19.75                  <NA>
## 203                                                 3.7                  0.74
## 204                                                4.25           8.138888889
## 205                                                17.6           1.497894737
## 206                                                5.75           2.978947368
## 207                                               10.05           2.963333333
## 208                                                7.35           2.049999999
## 209                                                 6.7           4.821578947
## 210                                               15.85                  3.16
## 211                                                 9.9           0.961818182
## 212                                                4.65           2.014666667
## 213                                               12.25                  <NA>
## 214                                                 9.2                  <NA>
## 215                                                 7.5           0.638666667
## 216                                                8.35           1.966666667
## 217                                                5.95           2.081666667
##     Physicians_1000ppl Death.rate..crude..per.1.000.people...SP.DYN.CDRT.IN.
## 1          0.245933333                                              8.133000
## 2          1.387615385                                              7.823500
## 3               1.4515                                              4.725300
## 4                 <NA>                                              5.340000
## 5                3.294                                              3.616667
## 6                0.154                                             10.626850
## 7          1.731333333                                              5.845500
## 8          3.860957143                                              7.739100
## 9          2.829428571                                              9.877350
## 10                <NA>                                              7.921250
## 11         3.297555556                                              6.575000
## 12         4.876736842                                              9.420000
## 13         3.411647059                                              6.085000
## 14         2.220333333                                              6.670600
## 15         0.981928571                                              2.130050
## 16         0.434631579                                              6.006450
## 17              2.1825                                              8.984800
## 18         4.598777778                                             13.959350
## 19         3.151789474                                              9.760000
## 20              1.0305                                              5.042150
## 21         0.056285714                                             10.263200
## 22                <NA>                                              7.720000
## 23         0.335846154                                              6.894550
## 24              0.7564                                              7.992650
## 25         1.696538462                                             11.019600
## 26              0.3445                                              9.099650
## 27              2.0644                                              6.551050
## 28                <NA>                                              5.629350
## 29         1.414071429                                              4.132350
## 30         3.736428571                                             15.585000
## 31         0.056076923                                             10.807900
## 32               0.059                                              9.837600
## 33         0.601888889                                              5.585650
## 34         0.215428571                                              6.324000
## 35         0.101777778                                             10.213850
## 36         2.311842857                                              7.410000
## 37                <NA>                                              3.410000
## 38              0.0597                                             13.600600
## 39               0.046                                             14.571350
## 40                <NA>                                              8.331753
## 41         1.774789474                                              6.050000
## 42         1.631055556                                              6.990000
## 43         1.821157895                                              5.417650
## 44         0.224333333                                              9.121100
## 45             0.16175                                             11.171200
## 46         0.141428571                                              8.103900
## 47               2.549                                              4.927050
## 48         0.156428571                                             10.701700
## 49         2.899058824                                             12.494750
## 50           7.0593125                                              9.025000
## 51                <NA>                                              8.700850
## 52         2.417666667                                              6.646300
## 53         3.852321053                                             10.645000
## 54         3.724117647                                              9.700000
## 55               0.187                                              9.133900
## 56               1.126                                             10.411300
## 57             1.35725                                              5.987550
## 58         1.888857143                                              5.056000
## 59         0.714714286                                              5.973950
## 60             2.20644                                              7.090300
## 61              0.2675                                             10.443250
## 62         0.054444444                                              7.389550
## 63         3.327833333                                             12.265000
## 64            0.152875                                             13.915500
## 65         0.040555556                                              8.744050
## 66                <NA>                                              8.065000
## 67              0.5638                                              7.494500
## 68         3.589111111                                              9.640000
## 69               3.335                                              8.840000
## 70                <NA>                                              3.120200
## 71         0.457828571                                              8.016900
## 72         0.098363636                                              8.590250
## 73               5.105                                             12.652900
## 74         3.908210526                                             10.910000
## 75         0.106333333                                              8.250100
## 76                <NA>                                              8.095750
## 77           5.8465625                                             10.740000
## 78                <NA>                                              8.445000
## 79                0.94                                              7.507550
## 80                <NA>                                              5.484900
## 81             0.70925                                              5.405700
## 82         0.127333333                                             11.299600
## 83         0.134333333                                             10.615700
## 84               1.028                                              7.689750
## 85               0.174                                              9.096250
## 86              0.4044                                              4.510100
## 87                <NA>                                              6.215000
## 88         3.144210526                                             13.410000
## 89         3.676294118                                              6.390000
## 90              0.7025                                              7.456900
## 91              0.3476                                              7.772600
## 92               1.064                                              5.107500
## 93               0.737                                              5.617600
## 94         3.555947368                                              6.505000
## 95               1.449                                             10.360800
## 96         3.545421053                                              5.340000
## 97         3.861263158                                             10.395000
## 98            0.493375                                              6.827800
## 99         2.294444444                                              9.975000
## 100        2.317384615                                              3.513800
## 101        3.798833333                                              8.660500
## 102        0.183888889                                              7.942400
## 103            0.23775                                              6.713200
## 104              3.449                                              8.670150
## 105        2.046833333                                              5.440000
## 106               <NA>                                              5.199150
## 107        2.118142857                                              2.055450
## 108        2.333538462                                              6.245000
## 109        0.342285714                                              7.785150
## 110        3.139277778                                             14.775000
## 111        2.350636364                                              4.802900
## 112              0.189                                             16.861100
## 113        0.029666667                                              9.997900
## 114        1.865166667                                              5.078050
## 115               <NA>                                              6.665000
## 116              4.101                                             13.960000
## 117        2.730126667                                              7.370000
## 118               <NA>                                              3.794650
## 119        0.166384615                                              7.565200
## 120           0.039125                                             10.274050
## 121        1.319277778                                              4.992200
## 122        1.621111111                                              3.131350
## 123        0.093933333                                             11.261350
## 124             2.8113                                              7.705000
## 125              0.564                                              6.965100
## 126        0.159857143                                              8.031800
## 127        1.575888889                                              7.875000
## 128        2.115777778                                              6.148300
## 129        0.584533333                                              5.061950
## 130           2.874875                                             13.412700
## 131            7.36225                                              9.500000
## 132             3.0641                                              6.366800
## 133        2.233210526                                             10.185000
## 134        0.672666667                                              5.925650
## 135        0.060333333                                             10.858550
## 136        0.559846154                                              9.262000
## 137            0.47375                                             10.599650
## 138              1.103                                              7.555400
## 139           0.677125                                              7.067850
## 140        3.181444444                                              8.630000
## 141               <NA>                                              5.430000
## 142        2.789894737                                              6.802500
## 143              0.727                                              4.893000
## 144        0.032833333                                             10.366100
## 145        0.359909091                                             14.311350
## 146        2.512916667                                             10.022668
## 147               <NA>                                              4.150000
## 148        4.257052632                                              8.295000
## 149        1.939277778                                              2.675700
## 150        0.821066667                                              7.452200
## 151             1.5242                                              8.675000
## 152        1.479470588                                              4.937550
## 153             0.0572                                              6.946150
## 154           1.785125                                              5.737350
## 155        1.337166667                                              6.563250
## 156        1.035153846                                              5.705450
## 157          2.3219375                                             10.495000
## 158        4.306222222                                             10.475000
## 159               <NA>                                              8.438850
## 160           2.800875                                              1.359200
## 161        2.455833333                                             13.160000
## 162        4.420333333                                             14.055000
## 163        0.080684615                                              7.929500
## 164        0.377714286                                              5.315250
## 165            5.16675                                              7.378947
## 166        0.438666667                                              6.832600
## 167        2.214142857                                              2.679550
## 168          0.1206625                                              6.993150
## 169        2.878142857                                             14.748500
## 170        1.341583333                                              7.810000
## 171        0.038916667                                             12.435300
## 172        1.891235294                                              4.775000
## 173               <NA>                                              4.443850
## 174        3.428052632                                             10.090000
## 175        2.648444444                                              9.650000
## 176        0.173142857                                              5.434650
## 177             0.0255                                             13.531400
## 178        0.772833333                                             10.888750
## 179               0.04                                             11.944850
## 180        3.783944444                                              8.820000
## 181        0.797166667                                              7.468900
## 182               2.95                                              9.297600
## 183        1.048908333                                              7.738950
## 184               <NA>                                              4.058200
## 185        0.833333333                                              8.293200
## 186        0.334333333                                              7.717800
## 187        0.921166667                                              7.579450
## 188        4.042444444                                              9.515000
## 189        4.017421053                                              8.100000
## 190        1.400111111                                              5.130750
## 191               1.79                                              5.600300
## 192        0.046666667                                              8.224050
## 193        0.515666667                                              6.658250
## 194        0.411692308                                              7.771700
## 195        0.059781818                                              9.738600
## 196        0.734857143                                              6.740150
## 197        2.261818182                                              7.104800
## 198             1.1282                                              5.748400
## 199        1.687555556                                              5.340250
## 200            2.28925                                              6.592100
## 201               <NA>                                              6.259700
## 202             1.0668                                             10.039400
## 203        0.162857143                                              8.253700
## 204        3.233416667                                             15.785000
## 205        1.983277778                                              1.266000
## 206        2.682421053                                              9.280000
## 207            3.08575                                              8.551900
## 208            4.77125                                              9.598600
## 209        2.565916667                                              4.965000
## 210        0.144166667                                              5.809900
## 211              1.664                                              6.155400
## 212              0.723                                              6.307650
## 213               <NA>                                              7.090000
## 214              1.971                                              3.603900
## 215              0.303                                              6.143050
## 216        0.139363636                                              9.017250
## 217             0.1294                                             11.854750
##     Rural.land.area..sq..km...AG.LND.TOTL.RU.K2.
## 1                                    636173.2292
## 2                                    27331.60161
## 3                                     2305478.23
## 4                                    198.2477988
## 5                                    409.4971338
## 6                                    1250999.883
## 7                                      396.33478
## 8                                    2735389.702
## 9                                    27342.21829
## 10                                   84.80002661
## 11                                   7650418.083
## 12                                   80030.88661
## 13                                   82218.65449
## 14                                   12279.91847
## 15                                    335.707418
## 16                                    79328.8629
## 17                                   248.5273247
## 18                                   201456.0045
## 19                                   22780.52409
## 20                                   21708.44766
## 21                                   113691.0639
## 22                                   20.30314892
## 23                                   39040.72891
## 24                                   1059511.349
## 25                                   49134.66048
## 26                                   572887.2314
## 27                                   8385496.484
## 28                                   147.7165994
## 29                                   5571.881756
## 30                                   108913.9424
## 31                                    273918.352
## 32                                   24150.62687
## 33                                   4043.854845
## 34                                   176180.6789
## 35                                   463837.3154
## 36                                   9197138.473
## 37                                   229.4268204
## 38                                   622165.0808
## 39                                   1272923.572
## 40                                          <NA>
## 41                                   723594.5312
## 42                                    8723723.06
## 43                                   1127197.644
## 44                                   1534.767163
## 45                                   2295898.507
## 46                                    340340.844
## 47                                   49804.71155
## 48                                   318023.6312
## 49                                   54777.08598
## 50                                   106664.7578
## 51                                    316.322036
## 52                                   8747.627334
## 53                                   73926.22029
## 54                                     40365.627
## 55                                    21571.7749
## 56                                   735.8386756
## 57                                   45389.62872
## 58                                   253213.9217
## 59                                   971206.1986
## 60                                   18676.37202
## 61                                   26898.23531
## 62                                   120001.0006
## 63                                   42656.57242
## 64                                   17073.22018
## 65                                   1124616.275
## 66                                   1385.837107
## 67                                   18883.78571
## 68                                   302765.4983
## 69                                   522936.0993
## 70                                   3984.443843
## 71                                   263337.7103
## 72                                   10197.83849
## 73                                   68068.72537
## 74                                   316383.4801
## 75                                   227830.2993
## 76                                    0.04744059
## 77                                   128795.9109
## 78                                   315961.3547
## 79                                   291.3748994
## 80                                   466.8133246
## 81                                   105527.1242
## 82                                   244205.7532
## 83                                    33293.4172
## 84                                   209904.1444
## 85                                    25688.0683
## 86                                   110411.1556
## 87                                   591.1958519
## 88                                   87565.24504
## 89                                   88792.01078
## 90                                   2956471.266
## 91                                   1820838.066
## 92                                   1601053.354
## 93                                   434269.6068
## 94                                   67698.15848
## 95                                   538.4579341
## 96                                   19195.22991
## 97                                   277070.5771
## 98                                   10006.15649
## 99                                   316736.2049
## 100                                  86673.39544
## 101                                  2636600.485
## 102                                  579896.1624
## 103                                  907.5566663
## 104                                  119025.2203
## 105                                   86793.3716
## 106                                         <NA>
## 107                                  16647.65026
## 108                                  186901.2745
## 109                                  228354.3777
## 110                                  62761.73831
## 111                                  8546.202041
## 112                                   30172.3371
## 113                                  95557.59073
## 114                                  1619018.279
## 115                                   117.014356
## 116                                  62585.34796
## 117                                  2247.078424
## 118                                  7.574165477
## 119                                  588612.8579
## 120                                  93702.75745
## 121                                  318197.5661
## 122                                  259.6535489
## 123                                  1251575.144
## 124                                  150.8971631
## 125                                  271.8843642
## 126                                  1045547.794
## 127                                  1523.755108
## 128                                   1920993.85
## 129                                  757.7457415
## 130                                  31803.71496
## 131                                  0.034954518
## 132                                  1549425.131
## 133                                    13146.802
## 134                                   409515.811
## 135                                   775002.468
## 136                                  660979.5454
## 137                                  827174.2883
## 138                                  15.05947124
## 139                                  140132.7835
## 140                                  26211.90392
## 141                                  18753.56673
## 142                                  270548.6286
## 143                                  118297.5466
## 144                                  1187981.381
## 145                                  882024.6559
## 146                                  23681.03685
## 147                                  477.1777027
## 148                                  306188.4558
## 149                                  309151.5535
## 150                                  821721.3635
## 151                                  480.9659618
## 152                                  73613.76711
## 153                                   462122.298
## 154                                  396430.5902
## 155                                  1281204.386
## 156                                  282119.5821
## 157                                  293194.0002
## 158                                   87685.7262
## 159                                  6516.709161
## 160                                  10591.24111
## 161                                  229905.7683
## 162                                  16224183.47
## 163                                  22337.27207
## 164                                  2817.356677
## 165                                  36.00050701
## 166                                  994.1657239
## 167                                  1908957.389
## 168                                  194448.5732
## 169                                  74452.57718
## 170                                  475.1735125
## 171                                  71910.77947
## 172                                  210.1625761
## 173                                  9.184403793
## 174                                  46876.44364
## 175                                  19401.47491
## 176                                  28414.49527
## 177                                  635102.2238
## 178                                  1204427.393
## 179                                  623624.9877
## 180                                  488925.7056
## 181                                   61386.1325
## 182                                  253.8698195
## 183                                  544.6162965
## 184                                   28.0700068
## 185                                  329.1274058
## 186                                  1859899.893
## 187                                  145003.1468
## 188                                  411134.3059
## 189                                  34763.58127
## 190                                  180422.9231
## 191                                   134045.209
## 192                                  880726.3952
## 193                                   491891.911
## 194                                  14909.69391
## 195                                  56198.45264
## 196                                  701.7861313
## 197                                  4364.437481
## 198                                  151132.5215
## 199                                   754924.702
## 200                                  463257.5154
## 201                                  910.9274136
## 202                                  39.09779159
## 203                                  201994.6724
## 204                                  569709.5062
## 205                                  77233.34084
## 206                                  218902.7593
## 207                                  8903098.427
## 208                                  173706.5677
## 209                                  412602.1407
## 210                                  12337.56625
## 211                                  899029.2577
## 212                                  297626.6621
## 213                                  273.7444318
## 214                                  4390.870891
## 215                                  452881.3089
## 216                                  740140.2194
## 217                                   386236.172
##     Surface.area..sq..km...AG.SRF.TOTL.K2.
## 1                                   652860
## 2                                    28750
## 3                                2381740.4
## 4                                      200
## 5                                      470
## 6                                  1246700
## 7                                      440
## 8                                  2780400
## 9                                    29740
## 10                                     180
## 11                                 7741220
## 12                                   83879
## 13                                   86600
## 14                                   13880
## 15                                  765.25
## 16                               148088.85
## 17                                     430
## 18                                207603.5
## 19                                 30568.2
## 20                                   22970
## 21                                  114760
## 22                                    2172
## 23                                38477.15
## 24                                 1098580
## 25                                   51210
## 26                                  581730
## 27                             8515502.389
## 28                                     150
## 29                                    5770
## 30                                110999.5
## 31                                  274216
## 32                                   27830
## 33                                    4030
## 34                                  181040
## 35                                  475440
## 36                                15639891
## 37                                     264
## 38                                  622980
## 39                                 1284000
## 40                                     198
## 41                                756307.4
## 42                              9562910.85
## 43                             1141580.008
## 44                                    1861
## 45                                 2344860
## 46                                  342000
## 47                                   51100
## 48                                  322460
## 49                                72331.25
## 50                                109883.5
## 51                                     444
## 52                                  9250.2
## 53                               78870.178
## 54                                   42920
## 55                                   23200
## 56                                     750
## 57                              68303.8926
## 58                                  256370
## 59                                 1001450
## 60                                   21040
## 61                                   28050
## 62                              121452.219
## 63                                   45285
## 64                                   17360
## 65                             1134645.505
## 66                               3130.5835
## 67                                   18270
## 68                                338395.3
## 69                             549102.0305
## 70                                    3471
## 71                                  267670
## 72                                   11300
## 73                                   69700
## 74                                  357314
## 75                                238538.6
## 76                                      10
## 77                                  131960
## 78                                  410450
## 79                                     340
## 80                                     540
## 81                                  108890
## 82                                  245860
## 83                                   36130
## 84                                  214970
## 85                                   27750
## 86                                  112490
## 87                             1103.736842
## 88                                   93030
## 89                                  103000
## 90                                 3287260
## 91                             1912981.514
## 92                                 1745150
## 93                                436069.8
## 94                                   70280
## 95                                     570
## 96                                   22070
## 97                                302068.9
## 98                                   10990
## 99                             377951.1135
## 100                                89157.6
## 101                              2724901.8
## 102                                 580370
## 103                                    810
## 104                                 120540
## 105                               100090.5
## 106                                   <NA>
## 107                                  17820
## 108                               199949.6
## 109                                 236800
## 110                                64593.4
## 111                                  10450
## 112                                  30360
## 113                                 111370
## 114                                1759540
## 115                                    160
## 116                                65294.5
## 117                                   2590
## 118                            30.15789474
## 119                                 587193
## 120                                 118480
## 121                              330614.95
## 122                                    300
## 123                                1240190
## 124                                    320
## 125                                    180
## 126                                1030700
## 127                                2020.35
## 128                             1964377.75
## 129                                    700
## 130                                33849.5
## 131                                60.3394
## 132                            1564118.775
## 133                                  13810
## 134                                 446550
## 135                                 799380
## 136                                 676590
## 137                                 824290
## 138                                     20
## 139                                 147180
## 140                                  41539
## 141                                  18580
## 142                                 267710
## 143                                 130370
## 144                                1267000
## 145                                 923770
## 146                                  25710
## 147                                    460
## 148                            505087.6945
## 149                                 309500
## 150                                 796100
## 151                                    460
## 152                                  75375
## 153                                 462840
## 154                               406751.3
## 155                                1285220
## 156                                 300000
## 157                               312687.5
## 158                               92185.66
## 159                                   8870
## 160                                  11556
## 161                                 238395
## 162                             17098246.5
## 163                                  26340
## 164                                   2840
## 165                                     60
## 166                                    960
## 167                                2149690
## 168                                 196710
## 169                                87854.5
## 170                                    460
## 171                                  72300
## 172                                  714.8
## 173                                     34
## 174                                  49032
## 175                                  20414
## 176                                  28900
## 177                                 637660
## 178                                1219090
## 179                                 646883
## 180                            505711.3541
## 181                                  65610
## 182                                    260
## 183                                    620
## 184                                     50
## 185                                    390
## 186                              2160514.5
## 187                                 163820
## 188                             488016.665
## 189                                41290.7
## 190                                 185180
## 191                               142023.2
## 192                                 947300
## 193                                 513120
## 194                                  14870
## 195                                  56790
## 196                                    750
## 197                                   5130
## 198                                 163610
## 199                                 785350
## 200                               488721.8
## 201                                    950
## 202                                     30
## 203                                 241550
## 204                                 603550
## 205                                98647.9
## 206                                 243610
## 207                                9781640
## 208                                 176220
## 209                               446031.4
## 210                                  12190
## 211                                 912050
## 212                              330983.94
## 213                                    350
## 214                            6020.263158
## 215                                 527970
## 216                                 752610
## 217                                 390760
# Rename columns with names that are likely to cause issues

names(data_some)[c(3,5,6,8,9,10,13,14,15)] <- c("HealthExp_PercGDP",
                                       "UNDP_PovRatio_PercPop",
                                       "SocLine_PovRatio_PercPop",
                                       "PrevHIV_PercPop1549",
                                       "IncTB_Per100k",
                                       "PrevDiab_PercPop2079",
                                       "Crude_Death_rate_Per1000",
                                       "Rural_Land_Area_Sq_Km",
                                       "Surface_Area_Sq_Km")
 
# # Turn the character columns in numeric apart from Country

data_some$HealthExp_PercGDP <- as.numeric(data_some$HealthExp_PercGDP)
data_some$GDP_per_Capita <- as.numeric(data_some$GDP_per_Capita)
data_some$UNDP_PovRatio_PercPop <- as.numeric(data_some$UNDP_PovRatio_PercPop)
data_some$SocLine_PovRatio_PercPop <- as.numeric(data_some$SocLine_PovRatio_PercPop)
data_some$Population_Rural <- as.numeric(data_some$Population_Rural)
data_some$PrevHIV_PercPop1549 <- as.numeric(data_some$PrevHIV_PercPop1549)
data_some$IncTB_Per100k <- as.numeric(data_some$IncTB_Per100k)
data_some$PrevDiab_PercPop2079 <- as.numeric(data_some$PrevDiab_PercPop2079)
data_some$Hospital_beds_1000ppl <- as.numeric(data_some$Hospital_beds_1000ppl)
data_some$Physicians_1000ppl <- as.numeric(data_some$Physicians_1000ppl)
data_some$Rural_Land_Area_Sq_Km <- as.numeric(data_some$Rural_Land_Area_Sq_Km)
data_some$Surface_Area_Sq_Km <- as.numeric(data_some$Surface_Area_Sq_Km)

Country1 <- data_some$Country

Before we start doing anything, we want to understand our data a bit better. Because we are using indicators, there might be high correlations between some of the variables in our dataset. If that’s the case, we want to address it and transform or drop variables before this becomes an issue in the analysis.

pairs(data_some[-1]) # pairs plot, notice we remove the Country column

data_some_corr <- as.data.frame(cor(data_some[-1], method = "pearson", use = "complete.obs")) # calculate Pearson's coefficient pairwise

Looking at the correlation patterns between the numerical variables, we notice that Population Total and Population Rural are highly correlated. Multidimensional poverty head count ratio (UNDP) is highly correlated with Poverty head count ratio at societal poverty line and, the Rural land area is highly correlated with the surface area. We consider “Highly correlated” variables with a Pearson’s Correlation Coefficient above 0.80.

To keep as much information as we can we:

# Drop variables listed in the paragraph above this chunk
library(dplyr)
data_some <- data_some %>% dplyr::select(-c(Population_Total,
                                     SocLine_PovRatio_PercPop,
                                     Surface_Area_Sq_Km))

# Re-examine correlation

pairs(data_some[-1]) # pairs plot, notice we remove the Country column

data_some_corr <- as.data.frame(cor(data_some[-1], method = "pearson", use = "complete.obs")) # calculate Pearson's coefficient pairwise

Now the correlations seems to never get so extreme.

Part B

At this stage, we want to address the missingness in the dataset. The R package MICE and the accompanying vignettes will be our tools. Through exploring the missingness pattern below, we notice that the missing values affect the two poverty ratio metrics and the HIV prevalence.

The code throws errors at us about matrix singularity, this is usually due to high collinearity (see troubleshooting). If we run the imputation adding variables one by one, we notice that it’s Population Rural causing issues. Therefore, we run the algorithm with the chosen method with everything but country and Population Rural in, and then with the Classification and Regression Trees method with it in.

library(mice)

md.pattern(data_some, rotate.names = TRUE) # examine missingness pattern

##    Country Crude_Death_rate_Per1000 Population_Rural Rural_Land_Area_Sq_Km
## 96       1                        1                1                     1
## 50       1                        1                1                     1
## 12       1                        1                1                     1
## 30       1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     1
## 13       1                        1                1                     1
## 1        1                        1                1                     1
## 3        1                        1                1                     1
## 2        1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     1
## 1        1                        1                1                     0
## 1        1                        1                0                     1
## 1        1                        1                0                     0
##          0                        0                2                     2
##    GDP_per_Capita PrevDiab_PercPop2079 IncTB_Per100k Physicians_1000ppl
## 96              1                    1             1                  1
## 50              1                    1             1                  1
## 12              1                    1             1                  1
## 30              1                    1             1                  1
## 1               1                    1             1                  1
## 1               1                    1             1                  1
## 1               1                    1             1                  1
## 1               1                    1             1                  1
## 13              1                    1             1                  0
## 1               1                    1             0                  1
## 3               1                    1             0                  0
## 2               1                    0             1                  0
## 1               0                    1             1                  1
## 1               0                    1             1                  0
## 1               0                    0             0                  0
## 1               1                    1             0                  0
## 1               1                    0             0                  0
## 1               1                    0             0                  0
##                 3                    5             8                 23
##    Hospital_beds_1000ppl HealthExp_PercGDP PrevHIV_PercPop1549
## 96                     1                 1                   1
## 50                     1                 1                   1
## 12                     1                 1                   0
## 30                     1                 1                   0
## 1                      1                 0                   0
## 1                      0                 1                   1
## 1                      0                 1                   0
## 1                      0                 0                   0
## 13                     0                 0                   0
## 1                      1                 0                   0
## 3                      0                 0                   0
## 2                      0                 0                   0
## 1                      1                 0                   0
## 1                      0                 0                   0
## 1                      0                 0                   0
## 1                      0                 0                   0
## 1                      0                 0                   0
## 1                      0                 0                   0
##                       26                27                  70
##    UNDP_PovRatio_PercPop    
## 96                     1   0
## 50                     0   1
## 12                     1   1
## 30                     0   2
## 1                      0   3
## 1                      0   2
## 1                      1   2
## 1                      1   3
## 13                     0   5
## 1                      0   4
## 3                      0   6
## 2                      0   6
## 1                      0   4
## 1                      0   6
## 1                      0   8
## 1                      0   7
## 1                      0   8
## 1                      0   9
##                      107 273
# We run the mice code with 0 iterations to get some info on how MICE plans to proceed

imputed_data <- mice(data_some, maxit=0)
## Warning: Number of logged events: 1
predM <- imputed_data$predictorMatrix # Extract predictor matrix (i.e. which variables predict which missing variables)
meth <- imputed_data$method           # Extract method of imputation per variable

# Perform multiple imputation
# Method 
# The m =  N means we get N datasets with N different imputations - if you have more than one then you have to pool to make inference. maxit = k means k number of iterations

imputed_data_1 <- mice(data_some[-1], m = 1, method = 'cart', maxit = 50, print = F)

# Plot the iteration trace plot for convergence diagnostics. (default = 5 iterations)

plot(imputed_data_1)

# Diagnostics of value range
#Stripplot diagnostics comparing imputed (red) and observed (blue) values for #variables with missing data. The substantial overlap between red and blue points #across all indicators indicates that the imputed values are consistent with the #observed data, suggesting plausible imputations

stripplot(imputed_data_1, pch=20, cex=2)

# Extract the actual element form the object resulting from mice()

imp_dataset_1 <- complete(imputed_data_1, 1) # take the first imputed dataset

# Let's re-add in the variables we removed (i.e. Country and Population Rural)

imp_dataset_1$Population_Rural <- data_some$Population_Rural

We can proceed to rescale the dataset, which is good practice before clustering to avoid magnifying or shrinking distances between features simply due to the way they get projected. There’s many ways to do this, we use the z-score standardisation.

# Z-score normalisation, that is subtracting the mean and dividing by the standard deviation

imp_dataset_scaled_1 <- as.data.frame(round(scale(imp_dataset_1), digits = 2)) 

# Let's re-add Country in

imp_dataset_scaled_1$Country <- data_some$Country

Part C

We proceed to cluster countries with hierarchical clustering with:

We follow:

# Create distance matrix

dist_scaled_1 <- dist(imp_dataset_scaled_1[-12], method = 'manhattan') # note we don't read "Country" in, it's not a feature to use

# 3. Perform hierarchical clustering (Complete linkage method) and create dendrogram

hclust_complete_1 <- hclust(dist_scaled_1, method = 'complete')
plot(hclust_complete_1, main = "Complete Linkage Dendrogram (CART)")

Now we need to decide where to cut the tree using dynamicTreeCut package. We use automated method, and a preference for less rather than more clusters.

# Load the dynamicTreeCut package
if (!requireNamespace("dynamicTreeCut", quietly = TRUE)) {
  install.packages("dynamicTreeCut")
}
library(dynamicTreeCut)


# Apply dynamic tree cut to obtain cluster memberships. 
#
#Here, we input a dendrogram only, as such, the algorithm uses the default "Tree" method. When both a dendrogram and a distance matrix are provided, the method automatically switches to "Hybrid".

cut_complete_1 <- cutreeDynamic(hclust_complete_1, deepSplit = 1, respectSmallClusters = TRUE, verbose = 0)
## Warning in cutreeDynamic(hclust_complete_1, deepSplit = 1, respectSmallClusters = TRUE, : cutreeDynamic: method "hybrid" requires a valid dissimilarity matrix "distM". 
## Defaulting to method "tree".
# Plot dendrograms with cluster rectangles. k is the number of clusters

plot(hclust_complete_1, main = "Hierarchical Clustering Dendrogram (CART)", xlab = "", sub = "")
rect.hclust(hclust_complete_1, k = max(cut_complete_1), border = "red")  # Use cut_complete_1

The subdivisions are highlighted in the graphs by rectangles.

Now we can reveal which countries fall within which cluster.

# Merge grouping to Country vector

clustering_complete_1 <- as.data.frame(cbind(cut_complete_1, imp_dataset_scaled_1$Country))

# Export data in excel form
library(writexl)
Indicators_df <- imp_dataset_1 
Indicators_df <- cbind(Country1 = Country1, Indicators_df)

names0 <- c('Australia', 'Canada', 'Spain', 'United Kingdom', 'United States')
Cluster0 <- Indicators_df[Indicators_df$Country1 %in% names0, ]

names1 <- c('Bangladesh', 'Cambodia', 'China', 'Djibouti', 'India', 'Indonesia', "Lao PDR", 'Myanmar', 'Nepal', 'Nigeria', 'Pakistan', 'Thailand', 'Viet Nam')
Cluster1 <- Indicators_df[Indicators_df$Country1 %in% names1, ]

names2 <- c('Azerbaijan', 'Ecuador', 'Egypt, Arab Rep.', 'Iraq', 'Turkiye')
Cluster2 <- Indicators_df[Indicators_df$Country1 %in% names2, ]

names3 <- c('Chile')
Cluster3 <- Indicators_df[Indicators_df$Country1 %in% names3, ]


#Compare summary statistics across the 4 clusters
# Function to summarize a data frame
summary_stats <- function(df, name) {
  df %>%
    summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max))) %>%
    mutate(DataFrame = name)  # Add a column for dataset name
}

# Apply function to each data frame
Cluster1_summary <- summary_stats(Cluster0[-1], "Cluster1")
Cluster2_summary <- summary_stats(Cluster1[-1], "Cluster2")
Cluster3_summary <- summary_stats(Cluster2[-1], "Cluster3")
Cluster4_summary <- summary_stats(Cluster3[-1], "Cluster4")

# Combine all summaries
comparison <- bind_rows(Cluster1_summary, Cluster2_summary, Cluster3_summary, Cluster4_summary)

# Reorder columns to move "DataFrame" to the first position
comparison <- comparison %>% select(DataFrame, everything())

# Print the summary comparison
print(comparison)
##   DataFrame HealthExp_PercGDP_mean HealthExp_PercGDP_sd HealthExp_PercGDP_min
## 1  Cluster1              10.854240             2.981532              8.864402
## 2  Cluster2               3.713824             1.179812              2.232583
## 3  Cluster3               4.598283             1.326172              3.322632
## 4  Cluster4               7.783749                   NA              7.783749
##   HealthExp_PercGDP_max GDP_per_Capita_mean GDP_per_Capita_sd
## 1             16.068211           45149.663         10208.683
## 2              6.688282            2307.789          1778.744
## 3              6.694435            5377.237          2710.282
## 4              7.783749           12636.477                NA
##   GDP_per_Capita_min GDP_per_Capita_max UNDP_PovRatio_PercPop_mean
## 1         29285.6946          55979.989                    0.38000
## 2           791.5463           6716.494                   20.10769
## 3          2556.2981           9879.269                    3.40000
## 4         12636.4768          12636.477                    2.50000
##   UNDP_PovRatio_PercPop_sd UNDP_PovRatio_PercPop_min UNDP_PovRatio_PercPop_max
## 1                0.2387467                       0.1                       0.7
## 2               14.9701626                       0.6                      43.6
## 3                3.4763487                       0.3                       8.6
## 4                       NA                       2.5                       2.5
##   Population_Rural_mean Population_Rural_sd Population_Rural_min
## 1              17922932            22923304              3335954
## 2             164772220           270313656               221745
## 3              19248533            20844093              4244328
## 4               2262842                  NA              2262842
##   Population_Rural_max PrevHIV_PercPop1549_mean PrevHIV_PercPop1549_sd
## 1             58560038                 0.279000              0.1283745
## 2            871947247                 1.370769              3.1391041
## 3             54663155                 0.154000              0.1207477
## 4              2262842                 0.375000                     NA
##   PrevHIV_PercPop1549_min PrevHIV_PercPop1549_max IncTB_Per100k_mean
## 1                   0.100                   0.400           8.220952
## 2                   0.100                  11.710         268.124542
## 3                   0.100                   0.370          40.285714
## 4                   0.375                   0.375          16.619048
##   IncTB_Per100k_sd IncTB_Per100k_min IncTB_Per100k_max
## 1         4.302596          3.833333          13.86190
## 2       102.483110         71.666667         454.00000
## 3        25.178343         15.904762          80.14286
## 4               NA         16.619048          16.61905
##   PrevDiab_PercPop2079_mean PrevDiab_PercPop2079_sd PrevDiab_PercPop2079_min
## 1                      7.73                1.676529                     5.75
## 2                      8.15                4.108984                     4.20
## 3                      9.91                5.741559                     4.20
## 4                     10.15                      NA                    10.15
##   PrevDiab_PercPop2079_max Hospital_beds_1000ppl_mean Hospital_beds_1000ppl_sd
## 1                    10.05                   3.141006                0.4012886
## 2                    19.35                   1.215939                0.8158868
## 3                    18.75                   2.426042                1.6768166
## 4                    10.15                   2.194211                       NA
##   Hospital_beds_1000ppl_min Hospital_beds_1000ppl_max Physicians_1000ppl_mean
## 1                 2.8261111                  3.837692               3.0323028
## 2                 0.2557895                  3.098333               0.5782396
## 3                 1.2236842                  5.260588               1.6879548
## 4                 2.1942105                  2.194211               1.7747895
##   Physicians_1000ppl_sd Physicians_1000ppl_min Physicians_1000ppl_max
## 1             0.5656104              2.3118429               3.783944
## 2             0.3741296              0.1870000               1.631056
## 3             1.1026093              0.7147143               3.411647
## 4                    NA              1.7747895               1.774789
##   Crude_Death_rate_Per1000_mean Crude_Death_rate_Per1000_sd
## 1                      8.127380                    1.108263
## 2                      7.886792                    2.171297
## 3                      5.614560                    0.429453
## 4                      6.050000                          NA
##   Crude_Death_rate_Per1000_min Crude_Death_rate_Per1000_max
## 1                      6.57500                      9.28000
## 2                      6.00645                     14.31135
## 3                      5.05600                      6.08500
## 4                      6.05000                      6.05000
##   Rural_Land_Area_Sq_Km_mean Rural_Land_Area_Sq_Km_sd Rural_Land_Area_Sq_Km_min
## 1                  5291696.7                4545825.3                 218902.76
## 2                  1330834.2                2371096.2                  21571.77
## 3                   499166.6                 362899.3                  82218.65
## 4                   723594.5                       NA                 723594.53
##   Rural_Land_Area_Sq_Km_max
## 1                 9197138.5
## 2                 8723723.1
## 3                  971206.2
## 4                  723594.5
#boxplot HealthExp_PercGDP

Cluster0$Cluster <- "Cluster0"
Cluster1$Cluster <- "Cluster1"
Cluster2$Cluster <- "Cluster2"
Cluster3$Cluster <- "Cluster3"

# Stack them together
combined_df1 <- bind_rows(
  Cluster0 %>% select(Cluster, Value = HealthExp_PercGDP),
  Cluster1 %>% select(Cluster, Value = HealthExp_PercGDP),
  Cluster2 %>% select(Cluster, Value = HealthExp_PercGDP),
  Cluster3 %>% select(Cluster, Value = HealthExp_PercGDP)
  
)
custom_colors <- c("#440154", "#3B528B", "#5DC863", "#FDE725" )
#View(combined_df)
ggplot(combined_df1, aes(x = Cluster, y = Value, fill = Cluster)) +
  geom_boxplot() +
  labs(title = "Health Expenditure (% GDP)",
       x = "",
       y = "") +
  theme_minimal() +
  scale_fill_manual(values = custom_colors) #scale_fill_viridis_d() 

  #scale_fill_brewer(palette = "Set3")  # Optional color scheme
#boxplot UNDP_PovRatio_PercPop 

Cluster0$Cluster <- "Cluster0"
Cluster1$Cluster <- "Cluster1"
Cluster2$Cluster <- "Cluster2"
Cluster3$Cluster <- "Cluster3"

# Stack them together
combined_df2 <- bind_rows(
  Cluster0 %>% select(Cluster, Value = UNDP_PovRatio_PercPop),
  Cluster1 %>% select(Cluster, Value = UNDP_PovRatio_PercPop),
  Cluster2 %>% select(Cluster, Value = UNDP_PovRatio_PercPop),
  Cluster3 %>% select(Cluster, Value = UNDP_PovRatio_PercPop)
  
)
custom_colors <- c("#440154", "#3B528B", "#5DC863", "#FDE725")
ggplot(combined_df2, aes(x = Cluster, y = Value, fill = Cluster)) +
  geom_boxplot() +
  labs(title = "Multidimensional Poverty Headcount Ratio (UNDP) (%
population)",
       x = "",
       y = "") +
  theme_minimal() +
  scale_fill_manual(values = custom_colors)

#boxplot Hospital_beds_1000ppl

Cluster0$Cluster <- "Cluster0"
Cluster1$Cluster <- "Cluster1"
Cluster2$Cluster <- "Cluster2"
Cluster3$Cluster <- "Cluster3"

# Stack them together
combined_df3 <- bind_rows(
  Cluster0 %>% select(Cluster, Value = Hospital_beds_1000ppl),
  Cluster1 %>% select(Cluster, Value = Hospital_beds_1000ppl),
  Cluster2 %>% select(Cluster, Value = Hospital_beds_1000ppl),
  Cluster3 %>% select(Cluster, Value = Hospital_beds_1000ppl)
  
)
custom_colors <- c("#440154", "#3B528B", "#5DC863", "#FDE725")
ggplot(combined_df3, aes(x = Cluster, y = Value, fill = Cluster)) +
  geom_boxplot() +
  labs(title = "Hospital beds (per 1,000 people)",
       x = "",
       y = "") +
  theme_minimal() +
  scale_fill_manual(values = custom_colors)

#boxplot Physicians_1000ppl

Cluster0$Cluster <- "Cluster0"
Cluster1$Cluster <- "Cluster1"
Cluster2$Cluster <- "Cluster2"
Cluster3$Cluster <- "Cluster3"

# Stack them together
combined_df4 <- bind_rows(
  Cluster0 %>% select(Cluster, Value = Physicians_1000ppl),
  Cluster1 %>% select(Cluster, Value = Physicians_1000ppl),
  Cluster2 %>% select(Cluster, Value = Physicians_1000ppl),
  Cluster3 %>% select(Cluster, Value = Physicians_1000ppl)
  
)

custom_colors <- c("#440154", "#3B528B", "#5DC863", "#FDE725")
#View(combined_df)
ggplot(combined_df4, aes(x = Cluster, y = Value, fill = Cluster)) +
  geom_boxplot() +
  labs(title = "Physicians (per 1,000 people)",
       x = "",
       y = "") +
  theme_minimal() +
  scale_fill_manual(values = custom_colors)

W H O

Indicators_df <- imp_dataset_1 
Indicators_df <- cbind(Country1 = Country1, Indicators_df)

WHOnames1 <- c('Australia', 'Cambodia', 'China', "Lao PDR", 'Myanmar', 'Thailand', 'Viet Nam')
Western_Pacific <- Indicators_df[Indicators_df$Country1 %in% WHOnames1, ]

WHOnames2 <- c('Bangladesh', 'India', 'Indonesia', 'Nepal')
South_East_Asia <- Indicators_df[Indicators_df$Country1 %in% WHOnames2, ]

WHOnames3 <- c('Djibouti', 'Nigeria')
Africa <- Indicators_df[Indicators_df$Country1 %in% WHOnames3, ]

WHOnames4 <- c('Egypt, Arab Rep.', 'Iraq', 'Pakistan')
Eastern_Mediterranean <- Indicators_df[Indicators_df$Country1 %in% WHOnames4, ]

WHOnames5 <- c('Azerbaijan', 'Spain', 'United Kingdom', 'Turkiye')
European <- Indicators_df[Indicators_df$Country1 %in% WHOnames5, ]

WHOnames6 <- c('Canada', 'United States', 'Ecuador', 'Chile')
Americas <- Indicators_df[Indicators_df$Country1 %in% WHOnames6, ]

#Compare summary statistics across the 4 clusters
# Function to summarize a data frame
summary_stats <- function(df, name) {
  df %>%
    summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max))) %>%
    mutate(DataFrame = name)  # Add a column for dataset name
}

# Apply function to each data frame
Western_Pacific_summary <- summary_stats(Western_Pacific[-1], "Western_Pacific")
South_East_Asia_summary <- summary_stats(South_East_Asia[-1], "South_East_Asia")
Africa_summary <- summary_stats(Africa[-1], "Africa")
Eastern_Mediterranean_summary <- summary_stats(Eastern_Mediterranean[-1], "Eastern_Mediterranean")
European_summary <- summary_stats(European[-1], "European")
Americas_summary <- summary_stats(Americas[-1], "Americas")

# Combine all summaries
WHOcomparison <- bind_rows(Western_Pacific_summary, South_East_Asia_summary, Africa_summary, Eastern_Mediterranean_summary,European_summary, Americas_summary)

# Reorder columns to move "DataFrame" to the first position
WHOcomparison <- WHOcomparison %>% select(DataFrame, everything())

# Print the summary comparison
print(WHOcomparison)
##               DataFrame HealthExp_PercGDP_mean HealthExp_PercGDP_sd
## 1       Western_Pacific               4.981070            2.2096588
## 2       South_East_Asia               3.247444            0.9578965
## 3                Africa               3.474284            0.3604135
## 4 Eastern_Mediterranean               3.621417            1.0595539
## 5              European               6.658399            3.1377497
## 6              Americas              10.255596            4.1883552
##   HealthExp_PercGDP_min HealthExp_PercGDP_max GDP_per_Capita_mean
## 1              2.752456              9.099192           10003.556
## 2              2.232583              4.474737            1661.456
## 3              3.219434              3.729135            1968.466
## 4              2.573061              4.691825            2728.457
## 5              3.322632              9.763403           21841.039
## 6              6.694435             16.068211           29778.765
##   GDP_per_Capita_sd GDP_per_Capita_min GDP_per_Capita_max
## 1       18556.95588           965.5861          51793.947
## 2         991.65541           791.5463           3082.969
## 3          56.09478          1928.8009           2008.131
## 4        1633.79708          1187.5561           4441.516
## 5       17710.56877          4964.7631          43234.431
## 6       24751.26287          5044.3379          55979.989
##   UNDP_PovRatio_PercPop_mean UNDP_PovRatio_PercPop_sd UNDP_PovRatio_PercPop_min
## 1                   12.15714               14.4980294                       0.6
## 2                   15.52500                8.7412337                       3.6
## 3                   38.30000                7.4953319                      33.0
## 4                   17.36667               18.2083314                       5.2
## 5                    0.50000                0.2160247                       0.3
## 6                    1.22500                1.2526638                       0.1
##   UNDP_PovRatio_PercPop_max Population_Rural_mean Population_Rural_sd
## 1                      38.3             113327375           233082845
## 2                      24.6             280343978           396776416
## 3                      43.6              48387682            68116921
## 4                      38.3              66390726            62508037
## 5                       0.8              11557405             6963589
## 6                       2.5              18328584            26888926
##   Population_Rural_min Population_Rural_max PrevHIV_PercPop1549_mean
## 1              3335954            639794213                0.7285714
## 2             22682431            871947247                0.2225000
## 3               221745             96553618                5.9050000
## 4             10577103            133931920                0.1066667
## 5              4244328             20945004                0.2237500
## 6              2262842             58560038                0.3362500
##   PrevHIV_PercPop1549_sd PrevHIV_PercPop1549_min PrevHIV_PercPop1549_max
## 1             0.51469015                     0.1                   1.550
## 2             0.10507934                     0.1                   0.315
## 3             8.20950973                     0.1                  11.710
## 4             0.01154701                     0.1                   0.120
## 5             0.14704733                     0.1                   0.390
## 6             0.09177645                     0.2                   0.400
##   IncTB_Per100k_mean IncTB_Per100k_sd IncTB_Per100k_min IncTB_Per100k_max
## 1          218.29660        161.54107          6.361905         454.00000
## 2          276.36905         52.14154        221.000000         341.90476
## 3          292.90476        104.51712        219.000000         366.80952
## 4          108.93651        142.19426         15.904762         272.61905
## 5           31.95714         32.44352         11.633333          80.14286
## 6           17.69286         19.01307          3.833333          44.90476
##   PrevDiab_PercPop2079_mean PrevDiab_PercPop2079_sd PrevDiab_PercPop2079_min
## 1                  6.628571                1.972278                     4.65
## 2                  8.912500                2.628490                     6.15
## 3                  5.525000                1.873833                     4.20
## 4                 16.000000                5.291266                     9.90
## 5                  7.362500                3.066316                     4.20
## 6                  8.437500                2.184176                     5.50
##   PrevDiab_PercPop2079_max Hospital_beds_1000ppl_mean Hospital_beds_1000ppl_sd
## 1                     9.70                  1.9782735                1.1645089
## 2                    12.35                  0.8540299                0.5873647
## 3                     6.85                  0.9530769                0.6407475
## 4                    19.35                  1.0748337                0.5412768
## 5                    11.20                  3.4962874                1.1916561
## 6                    10.15                  2.3642032                0.6819519
##   Hospital_beds_1000ppl_min Hospital_beds_1000ppl_max Physicians_1000ppl_mean
## 1                 0.6855556                  3.837692               1.0406912
## 2                 0.2557895                  1.640556               0.5404641
## 3                 0.5000000                  1.406154               0.2734545
## 4                 0.4747059                  1.526111               0.7575937
## 5                 2.6466667                  5.260588               2.8913920
## 6                 1.4731579                  2.963333               2.2653099
##   Physicians_1000ppl_sd Physicians_1000ppl_min Physicians_1000ppl_max
## 1            1.09685808              0.2154286              3.2975556
## 2            0.17637925              0.3476000              0.7025000
## 3            0.12226519              0.1870000              0.3599091
## 4            0.05608726              0.7147143              0.8210667
## 5            0.92379764              1.6875556              3.7839444
## 6            0.59374917              1.7747895              3.0857500
##   Crude_Death_rate_Per1000_mean Crude_Death_rate_Per1000_sd
## 1                      7.128864                   1.0685430
## 2                      7.075950                   0.7690557
## 3                     11.722625                   3.6610100
## 4                      6.347917                   0.9727937
## 5                      7.381312                   1.9596937
## 6                      6.766975                   1.5319842
##   Crude_Death_rate_Per1000_min Crude_Death_rate_Per1000_max
## 1                      6.30765                      9.26200
## 2                      6.00645                      7.77260
## 3                      9.13390                     14.31135
## 4                      5.61760                      7.45220
## 5                      5.34025                      9.28000
## 6                      5.05600                      8.55190
##   Rural_Land_Area_Sq_Km_mean Rural_Land_Area_Sq_Km_sd Rural_Land_Area_Sq_Km_min
## 1                  2604167.8                3829948.5                 176180.68
## 2                  1249192.7                1395250.7                  79328.86
## 3                   451798.2                 608432.1                  21571.77
## 4                   742399.1                 277117.7                 434269.61
## 5                   386243.0                 298274.8                  82218.65
## 6                  4769261.3                4948292.9                 253213.92
##   Rural_Land_Area_Sq_Km_max
## 1                 8723723.1
## 2                 2956471.3
## 3                  882024.7
## 4                  971206.2
## 5                  754924.7
## 6                 9197138.5

WORLD BANK

Indicators_df <- imp_dataset_1 
Indicators_df <- cbind(Country1 = Country1, Indicators_df)

WBnames1 <- c('Australia', 'Canada', 'Spain', 'United Kingdom', 'United States', 'Chile')
High_income_countries <- Indicators_df[Indicators_df$Country1 %in% WBnames1, ]

WBnames2 <- c('Azerbaijan', 'China', 'Turkiye', 'Thailand', 'Indonesia', 'Iraq', 'Ecuador', 'Egypt, Arab Rep.', 'Viet Nam')
Upper_Middle_income_countries <- Indicators_df[Indicators_df$Country1 %in% WBnames2, ]


WBnames3 <- c('Bangladesh', 'India', 'Nigeria', 'Pakistan', 'Myanmar', "Lao PDR", 'Nepal', 'Cambodia', 'Djibouti')
Lower_Middle_income_countries <- Indicators_df[Indicators_df$Country1 %in% WBnames3, ]

#Compare summary statistics across the 4 clusters
# Function to summarize a data frame
summary_stats <- function(df, name) {
  df %>%
    summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max))) %>%
    mutate(DataFrame = name)  # Add a column for dataset name
}

# Apply function to each data frame
High_income_countries_summary <- summary_stats(High_income_countries[-1], "High_income_countries")
Upper_Middle_income_countries_summary <- summary_stats(Upper_Middle_income_countries[-1], "Upper_Middle_income_countries")
Lower_Middle_income_countries_summary <- summary_stats(Lower_Middle_income_countries[-1], "Lower_Middle_income_countries")

# Combine all summaries
WBcomparison <- bind_rows(High_income_countries_summary, Upper_Middle_income_countries_summary, Lower_Middle_income_countries_summary)

# Reorder columns to move "DataFrame" to the first position
WBcomparison <- WBcomparison %>% select(DataFrame, everything())

# Print the summary comparison
print(WBcomparison)
##                       DataFrame HealthExp_PercGDP_mean HealthExp_PercGDP_sd
## 1         High_income_countries              10.342491             2.946684
## 2 Upper_Middle_income_countries               4.303142             1.133532
## 3 Lower_Middle_income_countries               3.615872             1.330347
##   HealthExp_PercGDP_min HealthExp_PercGDP_max GDP_per_Capita_mean
## 1              7.783749             16.068211           39730.799
## 2              2.829826              6.694435            4919.623
## 3              2.232583              6.688282            1401.204
##   GDP_per_Capita_sd GDP_per_Capita_min GDP_per_Capita_max
## 1        16110.8137         12636.4768          55979.989
## 2         2354.6114          2247.1656           9879.269
## 3          404.8989           791.5463           2008.131
##   UNDP_PovRatio_PercPop_mean UNDP_PovRatio_PercPop_sd UNDP_PovRatio_PercPop_min
## 1                  0.7333333                0.8914408                       0.1
## 2                  3.0000000                2.6758176                       0.3
## 3                 27.9333333               10.5524879                      16.4
##   UNDP_PovRatio_PercPop_max Population_Rural_mean Population_Rural_sd
## 1                       2.5              15312917            21476857
## 2                       8.6             106279662           203494638
## 3                      43.6             142418285           277913443
##   Population_Rural_min Population_Rural_max PrevHIV_PercPop1549_mean
## 1              2262842             58560038                0.2950000
## 2              4244328            639794213                0.4583333
## 3               221745            871947247                1.6072222
##   PrevHIV_PercPop1549_sd PrevHIV_PercPop1549_min PrevHIV_PercPop1549_max
## 1              0.1213260                     0.1                    0.40
## 2              0.5237485                     0.1                    1.55
## 3              3.7999688                     0.1                   11.71
##   IncTB_Per100k_mean IncTB_Per100k_sd IncTB_Per100k_min IncTB_Per100k_max
## 1           9.620635         5.154079          3.833333          16.61905
## 2         112.153439       110.671043         15.904762         341.90476
## 3         297.518519        89.035352        202.476191         454.00000
##   PrevDiab_PercPop2079_mean PrevDiab_PercPop2079_sd PrevDiab_PercPop2079_min
## 1                  8.133333                1.795736                     5.75
## 2                  8.927778                4.427902                     4.20
## 3                  8.350000                4.838647                     4.20
##   PrevDiab_PercPop2079_max Hospital_beds_1000ppl_mean Hospital_beds_1000ppl_sd
## 1                    10.15                  2.9832070                0.5274749
## 2                    18.75                  2.2479003                1.3237900
## 3                    19.35                  0.8562563                0.4728172
##   Hospital_beds_1000ppl_min Hospital_beds_1000ppl_max Physicians_1000ppl_mean
## 1                 2.1942105                  3.837692               2.8227172
## 2                 0.9026316                  5.260588               1.2952329
## 3                 0.2557895                  1.640556               0.4777548
##   Physicians_1000ppl_sd Physicians_1000ppl_min Physicians_1000ppl_max
## 1             0.7207557               1.774789              3.7839444
## 2             0.9734026               0.347600              3.4116471
## 3             0.2242525               0.187000              0.8210667
##   Crude_Death_rate_Per1000_mean Crude_Death_rate_Per1000_sd
## 1                      7.781150                    1.304549
## 2                      6.200144                    0.849277
## 3                      8.311089                    2.503344
##   Crude_Death_rate_Per1000_min Crude_Death_rate_Per1000_max
## 1                      6.05000                      9.28000
## 2                      5.05600                      7.77260
## 3                      6.00645                     14.31135
##   Rural_Land_Area_Sq_Km_mean Rural_Land_Area_Sq_Km_sd Rural_Land_Area_Sq_Km_min
## 1                  4530346.3                4473203.4                 218902.76
## 2                  1536657.0                2744770.5                  82218.65
## 3                   662973.9                 920908.6                  21571.77
##   Rural_Land_Area_Sq_Km_max
## 1                   9197138
## 2                   8723723
## 3                   2956471

U N

Indicators_df <- imp_dataset_1 
Indicators_df <- cbind(Country1 = Country1, Indicators_df)

UNnames1 <- c('Azerbaijan', 'China', 'Cambodia', 'Indonesia', "Lao PDR", 'Myanmar', 'Thailand', 'Viet Nam', 'Bangladesh', 'India', 'Nepal', 'Pakistan', 'Iraq', 'Turkiye')
Asia <- Indicators_df[Indicators_df$Country1 %in% UNnames1, ]

UNnames2 <- c('Egypt, Arab Rep.', 'Djibouti', 'Nigeria')
Afric <- Indicators_df[Indicators_df$Country1 %in% UNnames2, ]


UNnames3 <- c('Spain', 'United Kingdom')
Europe <- Indicators_df[Indicators_df$Country1 %in% UNnames3, ]

UNnames3 <- c('Canada', 'United States', 'Ecuador', 'Chile')
America <- Indicators_df[Indicators_df$Country1 %in% UNnames3, ]

UNnames3 <- c('Australia')
Ocean <- Indicators_df[Indicators_df$Country1 %in% UNnames3, ]

#Compare summary statistics across the 4 clusters
# Function to summarize a data frame
summary_stats <- function(df, name) {
  df %>%
    summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max))) %>%
    mutate(DataFrame = name)  # Add a column for dataset name
}

# Apply function to each data frame
Asia_summary <- summary_stats(Asia[-1], "Asia")
Afric_summary <- summary_stats(Afric[-1], "Afric")
Europe_summary <- summary_stats(Europe[-1], "Europe")
America_summary <- summary_stats(America[-1], "America")
Ocean_summary <- summary_stats(Ocean[-1], "Ocean")


# Combine all summaries
UNcomparison <- bind_rows(Asia_summary, Afric_summary, Europe_summary, America_summary, Ocean_summary)

# Reorder columns to move "DataFrame" to the first position
UNcomparison <- UNcomparison %>% select(DataFrame, everything())

# Print the summary comparison
print(UNcomparison)
##   DataFrame HealthExp_PercGDP_mean HealthExp_PercGDP_sd HealthExp_PercGDP_min
## 1      Asia               3.781164            1.1602748              2.232583
## 2     Afric               3.880131            0.7477191              3.219434
## 3    Europe               9.313902            0.6356892              8.864402
## 4   America              10.255596            4.1883552              6.694435
## 5     Ocean               9.099192                   NA              9.099192
##   HealthExp_PercGDP_max GDP_per_Capita_mean GDP_per_Capita_sd
## 1              6.688282            3239.277         2696.5217
## 2              4.691825            2164.410          341.6951
## 3              9.763403           36260.063         9863.2459
## 4             16.068211           29778.765        24751.2629
## 5              9.099192           51793.947                NA
##   GDP_per_Capita_min GDP_per_Capita_max UNDP_PovRatio_PercPop_mean
## 1           791.5463           9879.269                   13.89286
## 2          1928.8009           2556.298                   27.26667
## 3         29285.6946          43234.431                    0.45000
## 4          5044.3379          55979.989                    1.22500
## 5         51793.9466          51793.947                    0.70000
##   UNDP_PovRatio_PercPop_sd UNDP_PovRatio_PercPop_min UNDP_PovRatio_PercPop_max
## 1              13.38193181                       0.3                      38.3
## 2              19.83162458                       5.2                      43.6
## 3               0.07071068                       0.4                       0.5
## 4               1.25266383                       0.1                       2.5
## 5                       NA                       0.7                       0.7
##   Population_Rural_mean Population_Rural_sd Population_Rural_min
## 1             148644995           264821191              4244328
## 2              50479506            48302015               221745
## 3              10520144             1309503              9594186
## 4              18328584            26888926              2262842
## 5               3335954                  NA              3335954
##   Population_Rural_max PrevHIV_PercPop1549_mean PrevHIV_PercPop1549_sd
## 1            871947247                0.4507143             0.45797992
## 2             96553618                3.9700000             6.70303663
## 3             11446103                0.3475000             0.06010408
## 4             58560038                0.3362500             0.09177645
## 5              3335954                0.1000000                     NA
##   PrevHIV_PercPop1549_min PrevHIV_PercPop1549_max IncTB_Per100k_mean
## 1                   0.100                    1.55         217.173469
## 2                   0.100                   11.71         200.571429
## 3                   0.305                    0.39          12.747619
## 4                   0.200                    0.40          17.692857
## 5                   0.100                    0.10           6.361905
##   IncTB_Per100k_sd IncTB_Per100k_min IncTB_Per100k_max
## 1       131.904367         22.190476        454.000000
## 2       176.176750         15.904762        366.809524
## 3         1.575838         11.633333         13.861905
## 4        19.013070          3.833333         44.904762
## 5               NA          6.361905          6.361905
##   PrevDiab_PercPop2079_mean PrevDiab_PercPop2079_sd PrevDiab_PercPop2079_min
## 1                  8.585714                4.025134                     4.20
## 2                  9.933333                7.749570                     4.20
## 3                  7.025000                1.803122                     5.75
## 4                  8.437500                2.184176                     5.50
## 5                  6.500000                      NA                     6.50
##   PrevDiab_PercPop2079_max Hospital_beds_1000ppl_mean Hospital_beds_1000ppl_sd
## 1                    19.35                   1.645142               1.33780880
## 2                    18.75                   1.144088               0.56101226
## 3                     8.30                   3.038947               0.08485281
## 4                    10.15                   2.364203               0.68195187
## 5                     6.50                   3.837692                       NA
##   Hospital_beds_1000ppl_min Hospital_beds_1000ppl_max Physicians_1000ppl_mean
## 1                 0.2557895                  5.260588               0.9147435
## 2                 0.5000000                  1.526111               0.4205411
## 3                 2.9789474                  3.098947               3.2331827
## 4                 1.4731579                  2.963333               2.2653099
## 5                 3.8376923                  3.837692               3.2975556
##   Physicians_1000ppl_sd Physicians_1000ppl_min Physicians_1000ppl_max
## 1             0.8402214              0.2154286              3.4116471
## 2             0.2690312              0.1870000              0.7147143
## 3             0.7788947              2.6824211              3.7839444
## 4             0.5937492              1.7747895              3.0857500
## 5                    NA              3.2975556              3.2975556
##   Crude_Death_rate_Per1000_mean Crude_Death_rate_Per1000_sd
## 1                      6.866136                   1.0392442
## 2                      9.806400                   4.2091866
## 3                      9.050000                   0.3252691
## 4                      6.766975                   1.5319842
## 5                      6.575000                          NA
##   Crude_Death_rate_Per1000_min Crude_Death_rate_Per1000_max
## 1                      5.34025                      9.26200
## 2                      5.97395                     14.31135
## 3                      8.82000                      9.28000
## 4                      5.05600                      8.55190
## 5                      6.57500                      6.57500
##   Rural_Land_Area_Sq_Km_mean Rural_Land_Area_Sq_Km_sd Rural_Land_Area_Sq_Km_min
## 1                  1262047.3                2290076.7                  79328.86
## 2                   624934.2                 524426.4                  21571.77
## 3                   353914.2                 190935.1                 218902.76
## 4                  4769261.3                4948292.9                 253213.92
## 5                  7650418.1                       NA                7650418.08
##   Rural_Land_Area_Sq_Km_max
## 1                 8723723.1
## 2                  971206.2
## 3                  488925.7
## 4                 9197138.5
## 5                 7650418.1

Bubble plots - H5N1 Countries

library(ggplot2)

AvianCountriesDF<- Indicators_df[Indicators_df$Country1 %in% c('Australia', 'Canada', 'Spain', 'United Kingdom', 'United States','Bangladesh', 'Cambodia', 'China', 'Djibouti', 'India', 'Indonesia', "Lao PDR", 'Myanmar', 'Nepal', 'Nigeria', 'Pakistan', 'Thailand', 'Viet Nam', 'Azerbaijan', 'Ecuador', 'Egypt, Arab Rep.', 'Iraq', 'Turkiye', 'Chile'), ]

AvianCountriesDF$Hospital_beds_1000ppl <- round(AvianCountriesDF$Hospital_beds_1000ppl, 0)
AvianCountriesDF$Physicians_1000ppl <- round(AvianCountriesDF$Physicians_1000ppl, 0)

# Create bubble plot
ggplot(AvianCountriesDF, aes(x = HealthExp_PercGDP, y = UNDP_PovRatio_PercPop, size = Physicians_1000ppl, color = as.factor(Hospital_beds_1000ppl)))+ #as.factor(cyl))) +
  geom_point(alpha = 0.6) +
  scale_size_continuous(range = c(2, 12)) +
  labs(
    title = "Health Expenditure (% GDP) vs UNDP Poverty Ratio (% Population)",
    x = "Health Expenditure (% GDP)",
    y = "UNDP Poverty Ratio (% Population)",
    size = "Physicians per 1000 people",
    color = "Hospital beds per 1000 people"
  ) +
  theme_minimal()

Bubble plots - H5N1 Clusters

library(ggplot2)
library(ggrepel)
library(dplyr)

AvianClustersDF <- comparison %>%
  rename(Cluster = DataFrame) %>%           # rename the column
  mutate(Cluster = dplyr::recode_factor(Cluster,  # recode
                                        "Cluster1" = "Cluster 0",
                                        "Cluster2" = "Cluster 1",
                                        "Cluster3" = "Cluster 2",
                                        "Cluster4" = "Cluster 3"))

# Round numeric variables
AvianClustersDF$Hospital_beds_1000ppl_mean <- round(AvianClustersDF$Hospital_beds_1000ppl_mean, 0)
AvianClustersDF$Physicians_1000ppl_mean <- round(AvianClustersDF$Physicians_1000ppl_mean, 0)

# Plot
ggplot(AvianClustersDF, aes(
    x = HealthExp_PercGDP_mean,
    y = UNDP_PovRatio_PercPop_mean,
    size = Physicians_1000ppl_mean,
    color = as.factor(Hospital_beds_1000ppl_mean)
  )) +
  geom_point(alpha = 0.6) +
  geom_text(aes(label = Cluster), vjust = -1.5, size = 4, fontface = "bold", show.legend = FALSE) +
  scale_color_discrete(guide = guide_legend(override.aes = list(shape = 16, size = 5))) +
  scale_size_continuous(range = c(3, 13)) +
  labs(
    title = "Health Expenditure (% GDP) versus UNDP Poverty Ratio (% population)",
    x = "Health Expenditure (% GDP)",
    y = "UNDP Poverty Ratio (% population)",
    size = "Physicians \nper 1000 people",
    color = "Hospital Beds \nper 1000 people"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "right"
  )

library(ggplot2)
library(ggrepel)
library(dplyr)

AvianClustersDF1 <- comparison %>%
  rename(Cluster = DataFrame) %>%           # rename the column
  mutate(Cluster = dplyr::recode_factor(Cluster,  # recode
                                        "Cluster1" = "Cluster 0",
                                        "Cluster2" = "Cluster 1",
                                        "Cluster3" = "Cluster 2",
                                        "Cluster4" = "Cluster 3"))

# Round numeric variables
AvianClustersDF1$GDP_per_Capita_mean <- round(AvianClustersDF1$GDP_per_Capita_mean, 0)
AvianClustersDF1$Crude_Death_rate_Per1000_mean <-round(AvianClustersDF1$Crude_Death_rate_Per1000_mean, 0)

# Plot
ggplot(AvianClustersDF1, aes(
    x = PrevHIV_PercPop1549_mean,
    y = IncTB_Per100k_mean,
    size = GDP_per_Capita_mean,
    color = as.factor(Crude_Death_rate_Per1000_mean)
  )) +
  geom_point(alpha = 0.6) +
  geom_text(aes(label = Cluster), vjust = -1.5, size = 4, fontface = "bold", show.legend = FALSE) +
  scale_color_discrete(guide = guide_legend(override.aes = list(shape = 16, size = 5))) +
  scale_size_continuous(range = c(3, 13)) +
  labs(
    title = "HIV Prevalence in 15 - 49 Age Group (% population) versus \nTB Incidence per 100,000 people",
    x = "HIV Prevalence in 15 - 49 age group (% population)",
    y = "TB Incidence per 100,000 people",
    size = "GDP per capita",
    color = "Crude Death Rate \nper 1000 people"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.position = "right"
  )

Plot of CFR’s

library(ggplot2)
library(ggthemes)  # optional, for themes

cfr_data <- data.frame(
  Group = c(
    "Cluster 0", "Cluster 1", "Cluster 2", "Cluster 3",
    "Western Pacific", "South East Asia", "Africa (WHO)", "Eastern Mediterranean",
    "European (WHO)", "Americas (WHO)",
    "High Income", "Upper Middle Income", "Lower Middle Income",
    "Asia (UN)", "Africa (UN)", "Europe (UN)", "Americas (UN)", "Oceania",
    "Overall"
  ),
  CFR = c(
    8.25, 66.22, 34.26, 29.29,
    55.38, 81.23, 50.00, 33.76,
    34.12, 11.70,
    7.86, 52.08, 54.87,
    65.48, 33.58, 8.30, 11.70, 29.29,
    51.33
  ),
  Lower = c(
    1.23, 62.01, 29.63, 1.26,
    49.61, 75.61, 9.43, 29.04,
    18.64, 1.78,
    1.17, 48.60, 44.70,
    61.34, 28.85, 0.32, 1.78, 1.26,
    48.07
  ),
  Upper = c(
    24.87, 70.27, 39.10, 84.19,
    61.05, 86.10, 90.57, 38.70,
    52.35, 33.87,
    23.82, 55.54, 64.78,
    69.47, 38.54, 36.94, 33.87, 84.19,
    54.57
  ),
  Category = c(
    rep("Study Clusters", 4),
    rep("WHO Regions", 6),
    rep("World Bank", 3),
    rep("UN Regions", 5),
    "Overall"
  )
)
library(ggplot2)
library(viridis)

# Reorder categories and group labels
cfr_data$Category <- factor(
  cfr_data$Category,
  levels = c("Study Clusters", "WHO Regions", "World Bank", "UN Regions", "Overall")
)

# Reorder group levels to group by Category
cfr_data$Group <- factor(cfr_data$Group, levels = cfr_data$Group[order(cfr_data$Category, -cfr_data$CFR)])

cfr_data$Group <- factor(cfr_data$Group, levels = c(
  "Cluster 0", "Cluster 1", "Cluster 2", "Cluster 3", 
  "Western Pacific", "South East Asia", "Africa (WHO)", "Eastern Mediterranean",
  "European (WHO)", "Americas (WHO)",
  "High Income", "Upper Middle Income", "Lower Middle Income",
  "Asia (UN)", "Africa (UN)", "Europe (UN)", "Americas (UN)", "Oceania",
  "Overall"
))

ggplot(cfr_data, aes(x = Group, y = CFR, color = Category)) +
  geom_point(size = 4) +
  geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.3, alpha = 0.7, linewidth = 1.2) +
  geom_text(
    aes(label = sprintf("%.1f%%", CFR)),
    hjust = 0.5, vjust = -0.7, size = 3.2, color = "black"
  ) +
  coord_flip() +
  labs(
    title = "Case Fatality Rate (CFR) by Cluster with 95% Credible Intervals",
    x = "",
    y = "CFR (%)",
    color = "Clustering Scheme:"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "top",
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank()
  ) +
  scale_color_viridis_d(option = "C") 

Forest plot of CFRs

suppressMessages(library(tidyverse))
library(ggthemes)

# Your CFR data
cfr_data <- data.frame(
  Group = c(
    "Cluster 0", "Cluster 1", "Cluster 2", "Cluster 3",
    "Western Pacific", "South East Asia", "Africa (WHO)", "Eastern Mediterranean",
    "European (WHO)", "Americas (WHO)",
    "High Income", "Upper Middle Income", "Lower Middle Income",
    "Asia (UN)", "Africa (UN)", "Europe (UN)", "Americas (UN)", "Oceania",
    "Overall"
  ),
  CFR = c(
    8.25, 66.22, 34.26, 29.29,
    55.38, 81.23, 50.00, 33.76,
    34.12, 11.70,
    7.86, 52.08, 54.87,
    65.48, 33.58, 8.30, 11.70, 29.29,
    51.33
  ),
  Lower = c(
    1.23, 62.01, 29.63, 1.26,
    49.61, 75.61, 9.43, 29.04,
    18.64, 1.78,
    1.17, 48.60, 44.70,
    61.34, 28.85, 0.32, 1.78, 1.26,
    48.07
  ),
  Upper = c(
    24.87, 70.27, 39.10, 84.19,
    61.05, 86.10, 90.57, 38.70,
    52.35, 33.87,
    23.82, 55.54, 64.78,
    69.47, 38.54, 36.94, 33.87, 84.19,
    54.57
  ),
  Category = c(
    rep("Study Clusters", 4),
    rep("WHO Regions", 6),
    rep("World Bank", 3),
    rep("UN Regions", 5),
    "Overall"
  )
)

# Format labels
cfr_data <- cfr_data %>%
  mutate(
    variable = row_number(),
    estext = paste0(round(CFR, 2), " (", round(Lower, 2), ", ", round(Upper, 2), ")")
  )

# Custom theme
theme_ham <- function(base_size = 12, base_family = "sans"){
  ggthemes::theme_fivethirtyeight(base_size, base_family) +
    ggplot2::theme(
      panel.spacing = ggplot2::unit(1.5, 'lines'),
      panel.border = ggplot2::element_rect(color = "grey50", fill = NA, linewidth = 1, linetype = 1),
      plot.background = ggplot2::element_rect(fill='white', colour='white'),
      panel.background = ggplot2::element_rect(fill='white', colour='white'),
      strip.text = ggplot2::element_text(colour = 'white', face = 'bold'),
      axis.title = ggplot2::element_text(),
      strip.background = ggplot2::element_rect(colour = "grey50", fill = "grey50"),
      legend.background = ggplot2::element_blank(),
      panel.grid.major = ggplot2::element_line(linetype = 3, linewidth = 0.2)
    )
}

# Forest plot
ggplot(data = cfr_data, aes(x = variable)) +
  geom_hline(yintercept = 0) +
  geom_point(aes(y = CFR), size = 2, color = "black", shape = 5) +
  geom_linerange(
    aes(
      ymin = Lower,
      ymax = Upper
    ),
    linewidth = 0.5,
    color = "black"
  ) +
  labs(y = "Case Fatality Rate (%)", x = "") + 
  theme_ham() + 
  scale_x_continuous(
    breaks = cfr_data$variable,
    labels = cfr_data$Group,
    sec.axis = sec_axis(
      ~.,
      breaks = cfr_data$variable,
      labels = cfr_data$estext
    )
  ) +
  coord_flip(xlim = c(0.5, nrow(cfr_data) + 0.5))

library(tidyverse)
library(ggthemes)
library(viridis)

# Data
cfr_data <- data.frame(
  Group = c(
    "Cluster 0", "Cluster 1", "Cluster 2", "Cluster 3",
    "Western Pacific", "South East Asia", "Africa (WHO)", "Eastern Mediterranean",
    "European (WHO)", "Americas (WHO)",
    "High Income", "Upper Middle Income", "Lower Middle Income",
    "Asia (UN)", "Africa (UN)", "Europe (UN)", "Americas (UN)", "Oceania",
    "Overall"
  ),
  CFR = c(
    8.25, 66.22, 34.26, 29.29,
    55.38, 81.23, 50.00, 33.76,
    34.12, 11.70,
    7.86, 52.08, 54.87,
    65.48, 33.58, 8.30, 11.70, 29.29,
    51.33
  ),
  Lower = c(
    1.23, 62.01, 29.63, 1.26,
    49.61, 75.61, 9.43, 29.04,
    18.64, 1.78,
    1.17, 48.60, 44.70,
    61.34, 28.85, 0.32, 1.78, 1.26,
    48.07
  ),
  Upper = c(
    24.87, 70.27, 39.10, 84.19,
    61.05, 86.10, 90.57, 38.70,
    52.35, 33.87,
    23.82, 55.54, 64.78,
    69.47, 38.54, 36.94, 33.87, 84.19,
    54.57
  ),
  Category = c(
    rep("Study Clusters", 4),
    rep("WHO Regions", 6),
    rep("World Bank Regions", 3),
    rep("UN Regions", 5),
    "Overall"
  )
)

# Prepare labels

library(stringr)

cfr_data <- cfr_data %>%
  mutate(
    estext = sprintf("%.1f%%", CFR),  
    Group_clean = str_remove(Group, "\\s*\\(.*\\)"),  # strip (WHO), (UN)
    Category = factor(Category,
                      levels = c("UN Regions", "WHO Regions",
                                 "World Bank Regions", "Study Clusters", "Overall")),
    Group = factor(Group, levels = rev(Group))  # keep order for plotting
  )

# Custom theme
theme_ham <- function(base_size = 12, base_family = "sans"){
  ggthemes::theme_fivethirtyeight(base_size, base_family) +
    theme(
      panel.spacing.y = unit(0.5, 'lines'), # more vertical space
      panel.border = element_rect(color = "grey50", fill = NA, linewidth = 1),
      plot.background = element_rect(fill='white', colour='white'),
      panel.background = element_rect(fill='white', colour='white'),
      strip.text = element_text(colour = 'white', face = 'bold'),
      axis.title = element_text(),
      strip.background = element_rect(colour = "grey50", fill = "grey50"),
      legend.position = "none",
      panel.grid.major = element_line(linetype = 3, linewidth = 0.2)
    )
}

label_map <- setNames(as.character(cfr_data$Group_clean),
                      cfr_data$Group)

# Plot
ggplot(cfr_data, aes(x = Group, y = CFR, colour = Category)) +
  geom_point(size = 2, shape = 5) +
  geom_linerange(aes(ymin = Lower, ymax = Upper), linewidth = 0.5) +
  geom_text(aes(label = estext), hjust = 0.45, vjust = -0.5, 
            size = 3, colour = "black") +
  scale_x_discrete(labels = label_map) +  # match levels to clean labels
  labs(y = "Case Fatality Rate (%)", x = "") +
  coord_flip() +
  theme_ham() +
  scale_color_viridis_d(option = "C", end = 0.9) +
  facet_grid(Category ~ ., scales = "free_y", space = "free_y", switch = "y") +
  theme(strip.placement = "outside",
        strip.text.y.left = element_text(angle = 0))